System, method, and computer program product for machine unlearning on identity graph neural networks

Abstract

Systems, methods, and computer program products for machine unlearning on identity graph neural networks may obtain an identity graph including a plurality of graphs, each graph including a plurality of edges and a plurality of nodes for the plurality of edges, and, in each graph, each edge and each node is associated with a same identity; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components, such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network that corresponds to that biconnected component to generate a graph embedding and a local minima; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

Description

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR MACHINE UNLEARNING ON IDENTITY GRAPH NEURAL NETWORKSBACKGROUND1. Field

[0001] This disclosure relates to machine unlearning and, in some non-limiting embodiments or aspects, to systems, methods, and computer program products for machine unlearning on identity graph neural networks.2. Technical Considerations

[0002] Policies or regulations, such as the General Data Protection Regulation (GDPR) in the European Union, the Data Protection Bill in India, and / or the like, may demand that user personally identifiable information (PH) data be handled with extreme care.

[0003] A common application of data analytics is to predict an outcome using historical data that is already available. These kinds of recommendation or segmentation systems may rely on user specific data for the predictions to be accurate.

[0004] A user’s consent plays a role in procuring and using user specific PH data in a manner that complies with the aforementioned policies or regulations. Although a user may have consented to use of their information, that consent should not be taken for granted or treated as everlasting. In scenarios in which the user wishes to delete their data and / or account from a system, the user information for that user that was captured and used as a training dataset for machine learning models should be erased to remain compliant. Accordingly, with the applications of Machine Learning and Artificial Intelligence being extremely useful in areas, such as recommendation, prediction, and / or the like, the problem of user data protection may be exacerbated, particularly in scenarios where the user revokes the consent that was given earlier.SUMMARY

[0005] Accordingly, provided are improved systems, devices, products, apparatus, and / or methods for machine unlearning on identity graph neural networks.

[0006] According to some non-limiting embodiments or aspects, provided is a method, including: obtaining, with at least one processor, an identity graph includinga plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; applying, with the at least one processor, at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; splitting, with the at least one processor, the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, training, with the at least one processor, a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; training, with the at least one processor, an ensemble neural network on the graph embedding and the local minima of each biconnected component; and providing, with the at least one processor, the trained ensemble neural network.

[0007] In some non-limiting embodiments or aspects, obtaining, with the at least one processor, the identity graph including the plurality of graphs includes: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0008] In some non-limiting embodiments or aspects, the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).

[0009] In some non-limiting embodiments or aspects, applying, with the at least one processor, the at least one edge augmentation algorithm to the identity graph to makethe identity graph a biconnected identity graph further includes: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; and in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0010] In some non-limiting embodiments or aspects, determining whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph includes: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0011] In some non-limiting embodiments or aspects, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the method may further include applying, with the at least one processor, at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0012] In some non-limiting embodiments or aspects, providing, with the at least one processor, the trained ensemble neural network includes: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0013] According to some non-limiting embodiments or aspects, provided is a system, including: at least one processor coupled to a memory and configured to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

[0014] In some non-limiting embodiments or aspects, the at least one processor is configured to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0015] In some non-limiting embodiments or aspects, the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).

[0016] In some non-limiting embodiments or aspects, the at least one processor is configured to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's stronglyconnected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0017] In some non-limiting embodiments or aspects, the at least one processor is configured to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0018] In some non-limiting embodiments or aspects, if after wherein determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the at least one processor is further configured to apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0019] In some non-limiting embodiments or aspects, the at least one processor is configured to provide the trained ensemble neural network by: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0020] According to some non-limiting embodiments or aspects, provided is a computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

[0021] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH); and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0022] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0023] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0024] In some non-limiting embodiments or aspects, if after wherein determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the program instructions, when executed by the at least one processor, further cause the at least one processor to apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0025] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to provide the trained ensemble neural network by: receiving, with the at least oneprocessor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0026] Further embodiments or aspects are set forth in the following numbered clauses:

[0027] Clause 1. A method, comprising: obtaining, with at least one processor, an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; applying, with the at least one processor, at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; splitting, with the at least one processor, the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, training, with the at least one processor, a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; training, with the at least one processor, an ensemble neural network on the graph embedding and the local minima of each biconnected component; and providing, with the at least one processor, the trained ensemble neural network.

[0028] Clause 2. The method of clause 1 , wherein obtaining, with the at least one processor, the identity graph including the plurality of graphs includes: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; and deleting, from the previous identity graph, one or more previous edges of the pluralityof previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0029] Clause 3. The method of clauses 1 or 2, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).

[0030] Clause 4. The method of any of clauses 1 -3, wherein applying, with the at least one processor, the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph further includes: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; and in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0031] Clause 5. The method of any of clauses 1 -4, wherein determining whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph includes: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0032] Clause 6. The method of any of clauses 1 -5, wherein, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the method may further include applying, with the at least one processor, at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0033] Clause 7. The method of any of clauses 1 -6, wherein providing, with the at least one processor, the trained ensemble neural network includes: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0034] Clause 8. A system, comprising: at least one processor coupled to a memory and configured to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

[0035] Clause 9. The system of clause 8, wherein the at least one processor is configured to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previousnodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0036] Clause 10. The system of clauses 8 or 9, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).

[0037] Clause 1 1. The system of any of clauses 8-10, wherein the at least one processor is configured to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0038] Clause 12. The system of any of clauses 8-1 1 , wherein the at least one processor is configured to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0039] Clause 13. The system of any of clauses 8-12, wherein, if after wherein determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the at least one processor is further configured to apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0040] Clause 14. The system of any of clauses 8-13, wherein the at least one processor is configured to provide the trained ensemble neural network by: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0041] Clause 15. A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

[0042] Clause 16. The computer program product of clause 15, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of theplurality of identities, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH); and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

[0043] Clause 17. The computer program product of clauses 15 or 16 wherein the program instructions, when executed by the at least one processor, cause the at least one processor to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

[0044] Clause 18. The computer program product of any of clauses 15-17, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

[0045] Clause 19. The computer program product of any of clauses 15-18, wherein, if after wherein determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, one or more articulation points remain in the identity graph, the program instructions, when executed by the at least one processor, further cause the at least one processorto apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

[0046] Clause 20. The computer program product of any of clauses 15-19, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to provide the trained ensemble neural network by: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

[0047] These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of limits. As used in the specification and the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Additional advantages and details are explained in greater detail below with reference to the exemplary embodiments that are illustrated in the accompanying schematic figures, in which:

[0049] FIG. 1 is a diagram of non-limiting embodiments or aspects of an environment in which systems, devices, products, apparatus, and / or methods, described herein, may be implemented;

[0050] FIG. 2 is a diagram of non-limiting embodiments or aspects of components of one or more devices and / or one or more systems of FIG. 1 ;

[0051] FIG. 3 is a flowchart of non-limiting embodiments or aspects of a process for machine unlearning on identity graph neural networks;

[0052] FIG. 4 is a flowchart of non-limiting embodiments or aspects of a process for machine unlearning on identity graph neural networks; and

[0053] FIG. 5 is a block diagram of an implementation of non-limiting embodiments or aspects of a process for machine unlearning on identity graph neural networks.DESCRIPTION

[0054] It is to be understood that the present disclosure may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary and non-limiting embodiments or aspects. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered as limiting.

[0055] No aspect, component, element, structure, act, step, function, instruction, and / or the like used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more” and “at least one.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.) and may be used interchangeably with “one or more” or “at least one.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[0056] As used herein, the term “communication” may refer to the reception, receipt, transmission, transfer, provision, and / or the like, of data (e.g., information, signals, messages, instructions, commands, and / or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and / or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and / or transmit information to the other unit. This may refer to a direct or indirect connection (e.g., a direct communication connection, an indirect communication connection, and / or the like) that is wired and / or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and / or routedbetween the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit processes information received from the first unit and communicates the processed information to the second unit.

[0057] It will be apparent that systems and / or methods, described herein, can be implemented in different forms of hardware, software, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, it being understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0058] Some non-limiting embodiments or aspects are described herein in connection with thresholds. As used herein, satisfying a threshold may refer to a value being greater than the threshold, more than the threshold, higher than the threshold, greater than or equal to the threshold, less than the threshold, fewer than the threshold, lower than the threshold, less than or equal to the threshold, equal to the threshold, etc.

[0059] As used herein, the term “transaction service provider” may refer to an entity that receives transaction authorization requests from merchants or other entities and provides guarantees of payment, in some cases through an agreement between the transaction service provider and an issuer institution. For example, a transaction service provider may include a payment network such as Visa® or any other entity that processes transactions. The term “transaction processing system” may refer to one or more computing devices operated by or on behalf of a transaction service provider, such as a transaction processing server executing one or more software applications. A transaction processing system may include one or more processors and, in some non-limiting embodiments, may be operated by or on behalf of a transaction service provider.

[0060] As used herein, the term “account identifier” may include one or more primary account numbers (PANs), tokens, or other identifiers associated with a customer account. The term “token” may refer to an identifier that is used as asubstitute or replacement identifier for an original account identifier, such as a PAN. Account identifiers may be alphanumeric or any combination of characters and / or symbols. Tokens may be associated with a PAN or other original account identifier in one or more data structures (e.g., one or more databases and / or the like) such that they may be used to conduct a transaction without directly using the original account identifier. In some examples, an original account identifier, such as a PAN, may be associated with a plurality of tokens for different individuals or purposes.

[0061] As used herein, the terms “issuer institution,” “portable financial device issuer,” “issuer,” or “issuer bank” may refer to one or more entities that provide one or more accounts to a user (e.g., a customer, a consumer, an entity, an organization, and / or the like) for conducting transactions (e.g., payment transactions), such as initiating credit card payment transactions and / or debit card payment transactions. For example, an issuer institution may provide an account identifier, such as a PAN, to a user that uniquely identifies one or more accounts associated with that user. The account identifier may be embodied on a portable financial device, such as a physical financial instrument (e.g., a payment card), and / or may be electronic and used for electronic payments. In some non-limiting embodiments or aspects, an issuer institution may be associated with a bank identification number (BIN) that uniquely identifies the issuer institution. As used herein, the term “issuer institution system” may refer to one or more computer systems operated by or on behalf of an issuer institution, such as a server computer executing one or more software applications. For example, an issuer institution system may include one or more authorization servers for authorizing a payment transaction.

[0062] As used herein, the term “merchant” may refer to an individual or entity that provides goods and / or services, or access to goods and / or services, to users (e.g. customers) based on a transaction (e.g. a payment transaction). As used herein, the terms “merchant” or “merchant system” may also refer to one or more computer systems, computing devices, and / or software application operated by or on behalf of a merchant, such as a server computer executing one or more software applications. A “point-of-sale (POS) system,” as used herein, may refer to one or more computers and / or peripheral devices used by a merchant to engage in payment transactions with users, including one or more card readers, near-field communication (NFC) receivers, radio frequency identification (RFID) receivers, and / or other contactless transceivers or receivers, contact-based receivers, payment terminals, computers, servers, inputdevices, and / or other like devices that can be used to initiate a payment transaction. A POS system may be part of a merchant system. A merchant system may also include a merchant plug-in for facilitating online, Internet-based transactions through a merchant webpage or software application. A merchant plug-in may include software that runs on a merchant server or is hosted by a third-party for facilitating such online transactions.

[0063] As used herein, the term “mobile device” may refer to one or more portable electronic devices configured to communicate with one or more networks. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer (e.g., a tablet computer, a laptop computer, etc.), a wearable device (e.g., a watch, pair of glasses, lens, clothing, and / or the like), a personal digital assistant (PDA), and / or other like devices. The terms “client device” and “user device,” as used herein, refer to any electronic device that is configured to communicate with one or more servers or remote devices and / or systems. A client device or user device may include a mobile device, a network- enabled appliance (e.g., a network-enabled television, refrigerator, thermostat, and / or the like), a computer, a POS system, and / or any other device or system capable of communicating with a network.

[0064] As used herein, the term “computing device” may refer to one or more electronic devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and / or the like. A computing device may be a mobile device. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and / or the like), a PDA, and / or other like devices. A computing device may also be a desktop computer or other form of non-mobile computer.

[0065] As used herein, the term “payment device” may refer to a portable financial device, an electronic payment device, a payment card (e.g., a credit or debit card), a gift card, a smartcard, smart media, a payroll card, a healthcare card, a wristband, a machine-readable medium containing account information, a keychain device or fob, an RFID transponder, a retailer discount or loyalty card, a cellular phone, an electronic wallet mobile application, a PDA, a pager, a security card, a computer, an access card, a wireless terminal, a transponder, and / or the like. In some non-limiting embodimentsor aspects, the payment device may include volatile or nonvolatile memory to store information (e.g., an account identifier, a name of the account holder, and / or the like).

[0066] As used herein, the term "server" and / or “processor” may refer to or include one or more computing devices that are operated by or facilitate communication and processing for multiple parties in a network environment, such as the Internet, although it will be appreciated that communication may be facilitated over one or more public or private network environments and that various other arrangements are possible. Further, multiple computing devices (e.g., servers, POS devices, mobile devices, etc.) directly or indirectly communicating in the network environment may constitute a "system.” Reference to “a server” or “a processor,” as used herein, may refer to a previously-recited server and / or processor that is recited as performing a previous step or function, a different server and / or processor, and / or a combination of servers and / or processors. For example, as used in the specification and the claims, a first server and / or a first processor that is recited as performing a first step or function may refer to the same or different server and / or a processor recited as performing a second step or function.

[0067] As used herein, the term “acquirer” may refer to an entity licensed by the transaction service provider and / or approved by the transaction service provider to originate transactions using a portable financial device of the transaction service provider. Acquirer may also refer to one or more computer systems operated by or on behalf of an acquirer, such as a server computer executing one or more software applications (e.g., “acquirer server”). An “acquirer” may be a merchant bank, or in some cases, the merchant system may be the acquirer. The transactions may include original credit transactions (OCTs) and account funding transactions (AFTs). The acquirer may be authorized by the transaction service provider to sign merchants of service providers to originate transactions using a portable financial device of the transaction service provider. The acquirer may contract with payment facilitators to enable the facilitators to sponsor merchants. The acquirer may monitor compliance of the payment facilitators in accordance with regulations of the transaction service provider. The acquirer may conduct due diligence of payment facilitators and ensure that proper due diligence occurs before signing a sponsored merchant. Acquirers may be liable for all transaction service provider programs that they operate or sponsor. Acquirers may be responsible for the acts of its payment facilitators and the merchants it or its payment facilitators sponsor.

[0068] As used herein, the term “payment gateway” may refer to an entity and / or a payment processing system operated by or on behalf of such an entity (e.g., a merchant service provider, a payment service provider, a payment facilitator, a payment facilitator that contracts with an acquirer, a payment aggregator, and / or the like), which provides payment services (e.g., transaction service provider payment services, payment processing services, and / or the like) to one or more merchants. The payment services may be associated with the use of portable financial devices managed by a transaction service provider. As used herein, the term “payment gateway system” may refer to one or more computer systems, computer devices, servers, groups of servers, and / or the like operated by or on behalf of a payment gateway.

[0069] As used herein, the terms “authenticating system” and “authentication system” may refer to one or more computing devices that authenticate a user and / or an account, such as but not limited to a transaction processing system, merchant system, issuer system, payment gateway, a third-party authenticating service, and / or the like.

[0070] As used herein, the terms “request,” “response,” “request message,” and “response message” may refer to one or more messages, data packets, signals, and / or data structures used to communicate data between two or more components or units.

[0071] As used herein, the term “application programming interface” (API) may refer to computer code that allows communication between different systems or (hardware and / or software) components of systems. For example, an API may include function calls, functions, subroutines, communication protocols, fields, and / or the like usable and / or accessible by other systems or other (hardware and / or software) components of systems.

[0072] As used herein, the term “user interface” or “graphical user interface” refers to a generated display, such as one or more graphical user interfaces (GUIs) with which a user may interact, either directly or indirectly (e.g., through a keyboard, mouse, touchscreen, etc.).

[0073] Non-limiting embodiments or aspects of the present disclosure may provide systems, methods, and / or computer program products that obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, ineach graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

[0074] In this way, non-limiting embodiments or aspects of the present disclosure may enable efficient splitting of the data and feeding of the data into a network of models that are capable of individually training and feeding their respective knowledge to a master ensemble model in a manner that optimizes the data at multiple levels and imparts the knowledge while seamlessly enabling deleting of information, therefrom, and retraining the models in less time with reduced information loss or learning plateau. For example, data governance may be a factor for audits on user opt out honoring, and non-limiting embodiments or aspects of the present disclosure may provide an easier way to plug and unplug data into systems including complex neural networks. As an example, non-limiting embodiments or aspects may enhance a user’s privacy further and enable establishing a stronger trust amongst end users.

[0075] Referring now to FIG. 1 , FIG. 1 is a diagram of an example environment 100 in which devices, systems, methods, and / or products described herein, may be implemented. As shown in FIG. 1 , environment 100 includes transaction processing network 101 , which may include merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, issuer system 1 10, user device 1 12, and / or communication network 116. T ransaction processing network 101 , merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, issuer system 1 10, and / or user device 1 12, may interconnect (e.g., establish a connection to communicate, etc.) via wired connections, wireless connections, or a combination of wired and wireless connections.

[0076] Merchant system 102 may include one or more devices capable of receiving information and / or data from payment gateway system 104, acquirer system 106, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.) and / or communicating information and / or data to payment gateway system 104, acquirer system 106, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.). Merchant system 102 may include a device capable of receiving information and / or data from user device 1 12 via a communication connection (e.g., an NFC communication connection, an RFID communication connection, a Bluetooth® communication connection, etc.) with user device 1 12 and / or communicating information and / or data to user device 1 12 via the communication connection. For example, merchant system 102 may include a computing device, such as a server, a group of servers, a client device, a group of client devices, and / or other like devices. In some non-limiting embodiments or aspects, merchant system 102 may be associated with a merchant as described herein. In some non-limiting embodiments or aspects, merchant system 102 may include one or more devices, such as computers, computer systems, and / or peripheral devices capable of being used by a merchant to conduct a payment transaction with a user. For example, merchant system 102 may include a POS device and / or a POS system.

[0077] Payment gateway system 104 may include one or more devices capable of receiving information and / or data from merchant system 102, acquirer system 106, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.) and / or communicating information and / or data to merchant system 102, acquirer system 106, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.). For example, payment gateway system 104 may include a computing device, such as a server, a group of servers, and / or other like devices. In some non-limiting embodiments or aspects, payment gateway system 104 is associated with a payment gateway as described herein.

[0078] Acquirer system 106 may include one or more devices capable of receiving information and / or data from merchant system 102, payment gateway system 104, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.) and / or communicating information and / ordata to merchant system 102, payment gateway system 104, transaction service provider system 108, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.). For example, acquirer system 106 may include a computing device, such as a server, a group of servers, and / or other like devices. In some non-limiting embodiments or aspects, acquirer system 106 may be associated with an acquirer as described herein.

[0079] Transaction service provider system 108 may include one or more devices capable of receiving information and / or data from merchant system 102, payment gateway system 104, acquirer system 106, issuer system 110, and / or user device 1 12 (e.g., via communication network 1 16, etc.) and / or communicating information and / or data to merchant system 102, payment gateway system 104, acquirer system 106, issuer system 1 10, and / or user device 1 12 (e.g., via communication network 1 16, etc.). For example, transaction service provider system 108 may include a computing device, such as a server (e.g., a transaction processing server, etc.), a group of servers, and / or other like devices. In some non-limiting embodiments or aspects, transaction service provider system 108 may be associated with a transaction service provider as described herein. In some non-limiting embodiments or aspects, transaction service provider system 108 may include and / or access one or more internal and / or external databases including transaction data.

[0080] Issuer system 1 10 may include one or more devices capable of receiving information and / or data from merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or user device 1 12 (e.g., via communication network 1 16, etc.) and / or communicating information and / or data to merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or user device 1 12 (e.g., via communication network 1 16, etc.). For example, issuer system 110 may include a computing device, such as a server, a group of servers, and / or other like devices. In some non-limiting embodiments or aspects, issuer system 1 10 may be associated with an issuer institution as described herein. For example, issuer system 1 10 may be associated with an issuer institution that issued a payment account or instrument (e.g., a credit account, a debit account, a credit card, a debit card, etc.) to a user (e.g., a user associated with user device 1 12, etc.).

[0081] In some non-limiting embodiments or aspects, transaction processing network 101 includes a plurality of systems in a communication path for processing atransaction. For example, transaction processing network 101 can include merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or issuer system 1 10 in a communication path (e.g., a communication path, a communication channel, a communication network, etc.) for processing an electronic payment transaction. As an example, transaction processing network 101 can process (e.g., initiate, conduct, authorize, etc.) an electronic payment transaction via the communication path between merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or issuer system 1 10.

[0082] User device 1 12 may include one or more devices capable of receiving information and / or data from merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or issuer system 1 10 (e.g., via communication network 1 16, etc.) and / or communicating information and / or data to merchant system 102, payment gateway system 104, acquirer system 106, transaction service provider system 108, and / or issuer system 1 10 (e.g., via communication network 1 16, etc.). For example, user device 1 12 may include a client device and / or the like. In some non-limiting embodiments or aspects, user device 112 may be capable of receiving information (e.g., from merchant system 102, etc.) via a short range wireless communication connection (e.g., an NFC communication connection, an RFID communication connection, a Bluetooth® communication connection, and / or the like), and / or communicating information (e.g., to merchant system 102, etc.) via a short range wireless communication connection. In some nonlimiting embodiments or aspects, user device 1 12 may include an application associated with user device 1 12, such as an application stored on user device 1 12, a mobile application (e.g., a mobile device application, a native application for a mobile device, a mobile cloud application for a mobile device, an electronic wallet application, an issuer bank application, and / or the like) stored and / or executed on user device 1 12. In some non-limiting embodiments or aspects, user device 1 12 may be associated with a sender account and / or a receiving account in a payment network for one or more transactions in the payment network.

[0083] Communication network 1 16 may include one or more wired and / or wireless networks. For example, communication network 1 16 may include a cellular network (e.g., a long-term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multipleaccess (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, and / or the like, and / or a combination of these or other types of networks.

[0084] The number and arrangement of devices and systems shown in FIG. 1 is provided as an example. There may be additional devices and / or systems, fewer devices and / or systems, different devices and / or systems, or differently arranged devices and / or systems than those shown in FIG. 1 . Furthermore, two or more devices and / or systems shown in FIG. 1 may be implemented within a single device and / or system, or a single device and / or system shown in FIG. 1 may be implemented as multiple, distributed devices and / or systems. Additionally or alternatively, a set of devices and / or systems (e.g., one or more devices or systems) of environment 100 may perform one or more functions described as being performed by another set of devices and / or systems of environment 100.

[0085] Referring now to FIG. 2, FIG. 2 is a diagram of example components of a device 200. Device 200 may correspond to one or more devices of merchant system 102, one or more devices of payment gateway system 104, one or more devices of acquirer system 106, one or more devices of transaction service provider system 108, one or more devices of issuer system 1 10, and / or user device 1 12 (e.g., one or more devices of a system of user device 112, etc.). In some non-limiting embodiments or aspects, one or more devices of merchant system 102, one or more devices of payment gateway system 104, one or more devices of acquirer system 106, one or more devices of transaction service provider system 108, one or more devices of issuer system 1 10, and / or user device 1 12 (e.g., one or more devices of a system of user device 1 12, etc.) may include at least one device 200 and / or at least one component of device 200. As shown in FIG. 2, device 200 may include bus 202, processor 204, memory 206, storage component 208, input component 210, output component 212, and communication interface 214.

[0086] Bus 202 may include a component that permits communication among the components of device 200. In some non-limiting embodiments or aspects, processor 204 may be implemented in hardware, software, or a combination of hardware and software. For example, processor 204 may include a processor (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component (e.g., a field-programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), etc.) that can be programmed to perform a function. Memory 206 may include random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and / or instructions for use by processor 204.

[0087] Storage component 208 may store information and / or software related to the operation and use of device 200. For example, storage component 208 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, etc.), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of computer-readable medium, along with a corresponding drive.

[0088] Input component 210 may include a component that permits device 200 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally or alternatively, input component 210 may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output component 212 may include a component that provides output information from device 200 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), etc.).

[0089] Communication interface 214 may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface 214 may permit device 200 to receive information from another device and / or provide information to another device. For example, communication interface 214 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and / or the like.

[0090] Device 200 may perform one or more processes described herein. Device 200 may perform these processes based on processor 204 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), etc.) executing software instructionsstored by a computer-readable medium, such as memory 206 and / or storage component 208. A computer-readable medium (e.g., a non-transitory computer- readable medium) is defined herein as a non-transitory memory device. A non- transitory memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices.

[0091] Software instructions may be read into memory 206 and / or storage component 208 from another computer-readable medium or from another device via communication interface 214. When executed, software instructions stored in memory 206 and / or storage component 208 may cause processor 204 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments or aspects described herein are not limited to any specific combination of hardware circuitry and software.

[0092] Memory 206 and / or storage component 208 may include data storage or one or more data structures (e.g., a database, etc.). Device 200 may be capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or one or more data structures in memory 206 and / or storage component 208.

[0093] The number and arrangement of components shown in FIG. 2 are provided as an example. In some non-limiting embodiments or aspects, device 200 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 2. Additionally or alternatively, a set of components (e.g., one or more components) of device 200 may perform one or more functions described as being performed by another set of components of device 200.

[0094] Referring now to FIG. 3, FIG. 3 is a flowchart of non-limiting embodiments or aspects of a process 300 for machine unlearning on identity graph neural networks. In some non-limiting embodiments or aspects, one or more of the steps of process 300 may be performed (e.g., completely, partially, etc.) by transaction service provider system 108 (e.g., one or more devices of transaction service provider system 108, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 300 may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including transaction service provider system 108, such as merchant system 102 (e.g., one or more devices of merchant system 102),payment gateway system 104 (e.g., one or more devices of payment gateway system 104), acquirer system 106 (e.g., one or more devices of acquirer system 106), issuer system 110 (e.g., one or more devices of issuer system 110), and / or user device 1 12 (e.g., one or more devices of a system of user device 1 12).

[0095] As shown in FIG. 3, at step 302, process 300 includes obtaining an identity graph including a plurality of graphs. For example, transaction service provider system 108 may obtain an identity graph including a plurality of graphs. Each graph of the plurality of graphs may include a plurality of edges and a plurality of nodes for the plurality of edges. In each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph may be associated with a same identity (e.g., of a plurality of identities in the identity graph, etc.). Different graphs of the plurality of graphs may be associated with different identities, for example, each edge of the plurality of edges of a first graph and each node of the plurality of nodes of the first graph may be associated with a same first identity, and each edge of the plurality of edges of a second graph and each node of the plurality of nodes of the second graph may be associated with a same second identity different than the same first identity. For example, the plurality of graphs may be identified by identifying a source of a data point (e.g., nodes and edges that correspond to one identity, such as a unique account identifier, an email address, a name, etc.), and each of the data points from the same data source are identified or kept together as a graph of the plurality of graphs.

[0096] Further details regarding non-limiting embodiments or aspects of step 302 of process 300 are provided below with regard to FIG. 4.

[0097] As shown in FIG. 3, at step 304, process 300 includes applying at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph. For example, transaction service provider system 108 may apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph. As an example, transaction service provider system 108 may determine whether the identity graph includes one or more articulation points. If there are no articulation points in the identity graph, transaction service provider system 108 may determine that the identity graph is a biconnected graph. If the identity graph includes one or more articulation points, transaction service provider system 108 may determine that the identity graph is not a biconnected graph.

[0098] Transaction service provider system 108 may apply Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point A in the identity graph. For example, transaction service provider system 108 may construct a depth-first search (DFS) tree / forest based on the identity graph, such that a vertex or node u is a parent of another vertex or node v in the DFS tree / forest if v can be discovered by u. The vertex u from the DFS tree may be determined as an articulation point if, and only if, either of the following conditions are met: (i) u is the root of the DFS tree and has two child nodes or (ii) u is not the root of the DFS tree, but u has child v c no vertex in the subtree with v as the root has a back edge to one of the ancestors in the DFS tree of u.

[0099] Transaction service provider system 108 may determine if a new edge or edges can be added to the identity graph by applying one or more predetermined rules to the identity graph. For example, in response to determining the at least one articulation point in the identity graph, transaction service provider system 108 may determine whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph. As an example, transaction service provider system 108 may determine if a new edge or edges can be added to the identity graph from a set of parent nodes of the identity graph. In such an example, if each of a first identity and a second identity of the identity graph satisfies at least one predetermined rule, transaction service provider system 108 may attempt to augment a user vertex or node associated with the first identity and a user vertex or node associated with the second identity with an edge that indicates that the first identity and the second identity each satisfies the at least one predetermined rule. For example, if the identity graph indicates that a first identity uses a first payment card or PAN through a particular payment platform (e.g., Visa’s Stripe platform, etc.) and a second identity uses a second payment card or PAN through the same particular payment platform, transaction service provider system 108 may attempt to augment the user vertices or nodes associated with the first identity and the second identity with an edge that indicates that the first identity and the second identity each use the particular payment platform. In such an example, transaction service provider system 108 may augment a first user vertex or node associated with the first identity and a second user vertex or node associated with the second identity with an edge that indicates that the first identity and the second identity each satisfy the at least one predetermined rule (e.g.,each use the particular payment platform, etc.) by determining if a highest common ancestor (HCA) of the first user vertex or node and the second user vertex or node exists in the identity graph and, if such an HCA exists in the identity graph, adding an edge to the identity graph between the first user vertex or node and the second user vertex or node (e.g., citing the HCA -> lowest common ancestor (LCA) affiliations for that subnetwork, etc.).

[0100] Transaction service provider system 108 may use a biconnectivity augmentation algorithm, as described by Tsan-sheng Hsu in the paper entitled “Simpler and faster biconnectivity augmentation” in the Journal of Algorithms, Volume 45, Issue 1 , 2002, at Pages 55-71 , ISSN 0196-6774, the content of which is incorporated herein by reference in its entirety, to add one or more edges around one or more articulation points in the identity network. For example, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, and one or more articulation points remain in the identity graph, transaction service provider system 108 may apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph. As an example, for a graph G = (vertices V, edges E), the biconnectivity augmentation algorithm may output a smallest set of edges aug2(G), such that G u aug2(G) is biconnected. If G has less than 3 vertices or is biconnected, then the algorithm returns 0. If G is disconnected, the algorithm apply Fact 3.2 to find a set of edges E1 , such that G u E1 is connected; otherwise, the algorithm lets E1 = 0. The algorithm finds a vertex r in blk2(G u E1 ), such that blk2(G u E1 ) rooted at r is normalized. If r is a b-node, the algorithm does the following: applies Lemma 3.4 on G u E1 to find a set of edges E2 and returns E1 u E2. If r is a c-node, the algorithm does the following: if G u E1 is not balanced, the algorithm applies Fact 3.6 on G u E1 to find a set of edges E3, such that G u E1 u E3 is balanced, otherwise, the algorithm lets E3 = 0; roots blk2(G u E1 u E3) at r; applies Lemma 3.9 on G u E1 u E3 to find a set of edges E4; and returns E1 u E3 u E4.

[0101] As shown in FIG. 3, at step 306, process 300 includes splitting the biconnected identity graph into a plurality of biconnected components, such that there are no articulation points in each biconnected component. For example, transaction service provider system 108 may split the biconnected identity graph into a plurality ofbiconnected components, such that there are no articulation points in each biconnected component. As an example, and referring also to FIG. 5, transaction service provider system 108 may split the biconnected identity graph into multiple slices of biconnected graph components, such that there are no articulation points in each biconnected component.

[0102] As shown in FIG. 3, at step 308, process 300 includes, for each biconnected component, training a graph neural network (e.g., a GNN, etc.) of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component. For example, transaction service provider system 108 may, for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component. As an example, transaction service provider system 108 may identify a mother vertex (e.g., a vertex through which any other vertex can be reached in a graph, etc.) of each biconnected component and feed the mother vertex into the corresponding isolated GNN for predictive analysis, which may identify a local minima and generate a graph embedding that can be projected together into an ensemble model. In such an example, nodes of the graph embedding may be identity elements, and edges of the graph embedding may be confidence scores of relationships between those different nodes.

[0103] As shown in FIG. 3, at step 310, process 300 includes training an ensemble neural network on the graph embedding and the local minima of each biconnected component. For example, transaction service provider system 108 may train an ensemble neural network (e.g., a geometric neural network using a stochastic gradient descent (SGD) algorithm as an activation function, etc.) on the graph embedding and the local minima of each biconnected component. As an example, and referring again to FIG. 5, transaction service provider system 108 may feed the graph embedding and the local minima of each biconnected component into the ensemble neural network, and the ensemble neural network may identify a global minima and generate a further graph embedding in which nodes are the sliced and processed risk information for each biconnected graph component and edges are the Euclidean distance between each of these nodes (from the parent network). In such an example, the ensemble neural network may, thus, be trained by identifying the global minima and optimizingthe cost function (e.g., a number of epochs to achieve a better learning rate and / or other hyperparameter tuning, etc.) for the ensemble neural network. For example, the ensemble neural network may be trained in a same or similar manner, as described by Min Chen et al. in the paper entitled “Graph Unlearning”, arXiv:2103.14991 , the content of which is incorporated herein by reference in its entirety.

[0104] As shown in FIG. 3, at step 312, process 300 includes providing the trained ensemble neural network. For example, transaction service provider system 108 may provide the trained ensemble neural network. As an example, transaction service provider system 108 may store, in a memory and / or other data store, the trained ensemble neural network.

[0105] In some non-limiting embodiments or aspects, providing the trained ensemble neural network includes applying the trained ensemble neural network to one or more current transactions currently being processed in an electronic payment network (e.g., in transaction processing network 101 , etc.). For example, transaction service provider system 108 may receive transaction data associated with a current transaction (e.g., a transaction at a merchant, a transaction between multiple user accounts, etc.) in an electronic payment network. Transaction service provider system 108 may process, using the trained ensemble neural network, the transaction data to generate at least one prediction (e.g., a percentage, a likelihood, a yes or no, etc.) associated with the current transaction (e.g., a prediction of whether the transaction is a fraudulent transaction, etc.). For example, transaction service provider system 108 may provide, as input to the trained ensemble neural network, the transaction data, and receive, as output from the trained ensemble neural network, the at least one prediction associated with the current transaction. Transaction service provider system 108 may authorize or deny, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network. For example, transaction service provider system 108 may deny the current transaction in the electronic payment network in response to a prediction that the current transaction is a fraudulent transaction.

[0106] Referring now to FIG. 4, FIG. 4 is a flowchart of non-limiting embodiments or aspects of a process 400 for machine unlearning on identity graph neural networks. In some non-limiting embodiments or aspects, one or more of the steps of process 400 may be performed (e.g., completely, partially, etc.) by transaction service provider system 108 (e.g., one or more devices of transaction service provider system 108,etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 300 may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including transaction service provider system 108, such as merchant system 102 (e.g., one or more devices of merchant system 102), payment gateway system 104 (e.g., one or more devices of payment gateway system 104), acquirer system 106 (e.g., one or more devices of acquirer system 106), issuer system 110 (e.g., one or more devices of issuer system 110), and / or user device 1 12 (e.g., one or more devices of a system of user device 1 12).

[0107] As shown in FIG. 4, at step 402, process 400 includes obtaining a previous identity graph including a plurality of previous graphs. For example, transaction service provider system 108 may obtain a previous identity graph including a plurality of previous graphs. Each previous graph of the plurality of previous graphs may include a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges. In each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with a same identity of the plurality of identities (e.g., of a plurality of identities in the previous identity graph, etc.). Different graphs of the plurality of previous graphs may be associated with different identities, for example, each previous edge of the plurality of previous edges of a first previous graph and each previous node of the plurality of previous nodes of the first previous graph may be associated with a same first identity and each previous edge of the plurality of previous edges of a second previous graph and each previous node of the plurality of previous nodes of the second previous graph may be associated with a same second identity different than the same first identity. For example, the plurality of previous graphs may be identified by identifying a source of a data point (e.g., previous nodes and previous edges that correspond to one identity, such as a unique account identifier, an email address, a name, etc.), and each of the data points from the same data source are identified or kept together as a previous graph of the plurality of previous graphs. In such an example, the previous graph may have been previously used to train the ensemble model described herein (e.g., used to train a previous version of the ensemble model, etc.).

[0108] As shown in FIG. 4, at step 404, process 400 includes receiving a request to delete information associated with at least one identity of the plurality of identities. For example, transaction service provider system 108 may receive a request to deleteinformation associated with at least one identity of the plurality of identities. As an example, transaction service provider system 108 may receive, from a user associated with an identity element of the identity network such as a unique account identifier, an email address, a name, and / or the like (e.g., via user device 1 12, etc.), a request to delete information associated with the identity element of the user (e.g., associated with the unique account identifier of the user, the email address of the user, a name of the user, etc.). For example, the user may revoke a previous consent to use the information associated with the user, such as personally identifiable information (PH) and / or the like.

[0109] As shown in FIG. 4, at step 406, process 400 includes deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the updated identity graph including the plurality of graphs. For example, transaction service provider system 108 may delete, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the updated identity graph including the plurality of graphs. As an example, transaction service provider system 108 may delete the information associated with the identity element of the user (e.g., associated with the unique account identifier of the user, the email address of the user, a name of the user, etc.) from the previous identity graph to generate an updated identity graph without the user’s information therein. Accordingly, transaction processing system 108 may apply the process 300 for machine unlearning on identity graph neural networks to retrain the ensemble model with the updated identity graph without the user’s information therein.

[0110] Although embodiments or aspects have been described in detail for the purpose of illustration and description, it is to be understood that such detail is solely for that purpose and that embodiments or aspects are not limited to the disclosed embodiments or aspects, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment or aspect can be combined with one or more features of any other embodiment or aspect. In fact, anyof these features can be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

Claims

WHAT IS CLAIMED IS:1 . A method, comprising: obtaining, with at least one processor, an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; applying, with the at least one processor, at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; splitting, with the at least one processor, the biconnected identity graph into a plurality of biconnected components, such that there are no articulation points in each biconnected component; for each biconnected component, training, with the at least one processor, a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; training, with the at least one processor, an ensemble neural network on the graph embedding and the local minima of each biconnected component; and providing, with the at least one processor, the trained ensemble neural network.

2. The method of claim 1 , wherein obtaining, with the at least one processor, the identity graph including the plurality of graphs includes: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; anddeleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

3. The method of claim 2, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).

4. The method of claim 1 , wherein applying, with the at least one processor, the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph further includes: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; and in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

5. The method of claim 4, wherein determining whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph includes: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the secondidentity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

6. The method of claim 5, wherein, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, and one or more articulation points remain in the identity graph, the method may further include applying, with the at least one processor, at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

7. The method of claim 1 , wherein providing, with the at least one processor, the trained ensemble neural network includes: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

8. A system, comprising: at least one processor coupled to a memory and configured to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component;for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

9. The system of claim 8, wherein the at least one processor is configured to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality of previous edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities; and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

10. The system of claim 9, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH).1 1 . The system of claim 8, wherein the at least one processor is configured to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph;in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

12. The system of claim 1 1 , wherein the at least one processor is configured to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph; in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

13. The system of claim 12, wherein, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, and one or more articulation points remain in the identity graph, the at least one processor is further configured to apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

14. The system of claim 8, wherein the at least one processor is configured to provide the trained ensemble neural network by: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network;processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

15. A computer program product including a non-transitory computer readable medium including program instructions which, when executed by at least one processor, cause the at least one processor to: obtain an identity graph including a plurality of graphs, wherein each graph of the plurality of graphs includes a plurality of edges and a plurality of nodes for the plurality of edges, and wherein, in each graph, each edge of the plurality of edges of that graph and each node of the plurality of nodes of that graph is associated with a same identity of a plurality of identities; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network of a plurality of graph neural networks that corresponds to that biconnected component on that biconnected component to generate a graph embedding and a local minima of that biconnected component; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

16. The computer program product of claim 15, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to obtain the identity graph including the plurality of graphs by: obtaining a previous identity graph including a plurality of previous graphs, wherein each previous graph of the plurality of previous graphs includes a plurality of previous edges and a plurality of previous nodes for the plurality of previous edges, and wherein, in each previous graph, each previous edge of the plurality ofprevious edges in that previous graph and each previous node of the plurality of previous nodes in that previous graph is associated with the same identity of the plurality of identities; receiving a request to delete information associated with at least one identity of the plurality of identities, wherein the information associated with the at least one identity of the plurality of identities includes personal identifiable information (PH); and deleting, from the previous identity graph, one or more previous edges of the plurality of previous edges associated with the at least one identity and one or more previous nodes of the plurality of previous nodes associated with the at least one identity to generate the identity graph including the plurality of graphs.

17. The computer program product of claim 15, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to apply the at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph by: applying Tarjan's strongly connected components algorithm to the identity graph to determine whether there is at least one articulation point in the identity graph; in response to determining the at least one articulation point in the identity graph, determining whether one or more edges can be added to the identity graph around the at least one articulation point by applying one or more predetermined rules to the identity graph.

18. The computer program product of claim 17, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to determine whether one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph by: determining whether each of a first identity associated with a first node of the identity graph and a second identity associated with a second node of the identity graph satisfies at least one predetermined rule; in response to determining that each of the first identity and the second identity of the identity graph satisfies the at least one predetermined rule, determining whether a highest common ancestor of the first node associated with the first identityand the second node associated with the second identity exists in the identity graph; and in response to determining that the highest common ancestor of the first node associated with the first identity and the second node associated with the second identity exists in the identity graph, adding an edge to the identity graph between the first node and the second node.

19. The computer program product of claim 18, wherein, if after determining whether the one or more edges can be added to the identity graph by applying the one or more predetermined rules to the identity graph, and one or more articulation points remain in the identity graph, the program instructions, when executed by the at least one processor, further cause the at least one processor to apply at least one biconnectivity augmentation algorithm to the identity graph to add edges around the one or more articulation points that remain in the identity graph to make the identify graph the biconnected identity graph.

20. The computer program product of claim 15, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to provide the trained ensemble neural network by: receiving, with the at least one processor, transaction data associated with a current transaction in an electronic payment network; processing, with the at least one processor, using the trained ensemble neural network, the transaction data to generate at least one prediction associated with the current transaction; and authorizing or denying, with the at least one processor, based on the at least one prediction associated with the current transaction, the current transaction in the electronic payment network.

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