93 results about "Goal graph" patented technology

The invention discloses a fraudulent user identification method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the fraudulent user identification method comprises the steps: obtaining communication data of a to-be-identified user; establishing a target graph structure based on the communication data; mapping each node in the target graph structure to a hyperbolic space, and in the hyperbolic space, outputting a target embedding representation of the target graph structure through a hyperbolic graph convolution model; compared with Euclidean space, the hyperbolic space is usually considered to be more suitable for representing high-dimensional or hierarchical data, and in the hyperbolic space, target embedding representation more suitable for a task scene of fraudulent user identification can be obtained; based on the target embedded representation, determining a user classification result, namely accurately identifying whether the user to be identified is a fraud user or a normal user; therefore, the identification accuracy of the fraudulent user can be improved.

The invention relates to the technical field of attribute graph collection and release, in particular to an attribute graph collection and release method, system and device based on local differential privacy and a medium. Receiving disturbed data from each client; based on all disturbed data, estimating community division of the original graph structure and triangular counting of each node; obtaining a target graph structure based on community division and triangular counting; and on the basis of the target graph structure and the disturbed data, node attributes are optimized, so that node attribute distribution and community division are kept consistent, and a final publishable target attribute graph is obtained. The method has the advantages that strict requirements of local differential privacy are met on the privacy protection level, and multi-dimensional features such as a community structure, triangular counting and node attribute homogeneity are reserved in the published attribute graph at the same time; and the practical value of the synthetic data in applications such as downstream graph analysis, community discovery and personalized service is greatly improved.

The embodiment of the invention discloses a knowledge graph construction method and device, a storage medium and a terminal. Extracting a plurality of to-be-linked entities in the current text and to-be-linked relationships among the to-be-linked entities; matching each to-be-linked entity with each target entity in the target knowledge graph, and determining the to-be-linked entities meeting a first matching condition as to-be-updated entities; matching each to-be-linked relationship with a target relationship between target entities in the target knowledge graph, and determining the to-be-linked relationship meeting a second matching condition as a to-be-updated relationship; and updating the entity to be updated and all relation triples of the relation to be updated to the target knowledge graph. Through the active matching mechanism based on the existing knowledge of the target map, entities and relationships which exist in the current text and conform to the current semantic specification, structural constraint and knowledge rule of the target knowledge map can be effectively screened out, and the perception and adaptability to the dynamic change of the knowledge rule are remarkably enhanced.

The invention provides a chart question and answer data set generation method and device and a storage medium, and the method comprises the steps: combining the content of a paper of a target domain with the information of a plurality of charts in the paper, and determining a chart with the highest correlation with the research conclusion of the paper as a target chart; by combining content analysis of papers, under the condition of not only depending on the similarity of image features, the accuracy of chart selection is improved, and it is ensured that the supporting effect of the finally selected chart on a research conclusion is maximized. Then, based on the association between the target chart and the research conclusion of the paper, generating a question and answer pair corresponding to the target chart; according to the method, the application range of chart questions and answers is expanded, information in the chart can be deeply mined, scientificity and accuracy of chart selection and question and answer generation are ensured, the chart question and answer data set of the target field is created based on the multiple question and answer pairs, and more comprehensive analysis of researchers is supported.

Provided in the present application is a method for fusing operators in a computational graph, which method is applied to an AI compiling process. The method comprises: acquiring a target graph, wherein the target graph comprises a plurality of nodes and at least one edge, and the nodes are associated with operators in a computational graph; determining computation types of two nodes connected to a first edge, wherein computation logic of two operators corresponding to the two nodes both comprise a loop statement; and on the basis of a first computation type and a second computation type, fusing the loop statement of one operator into the loop statement of the other operator, so as to obtain a fused operator which is obtained by means of fusing the two operators. In the solution provided in the present application, it is not necessary to compress all loop statements of operators into one dimension and recalculate indexes for the operators, instead, two operators can be fused by means of computation types of the two operators, so that a high-performance fused operator can be obtained.

The embodiment of the invention provides an incremental query method for graph data, which comprises the following steps of: determining a target graph query which relates to an N-hop query; obtaining incremental graph data, wherein the incremental graph data comprises a plurality of incremental edges and incremental nodes connected with the incremental edges; and performing N-hop neighbor expansion by taking the incremental node as a starting point to obtain an expanded sub-graph. And in the expanded sub-graph, executing matching for the target graph query to obtain a query result.

The application discloses a trigger node detection method and device, equipment and a storage medium, and relates to the technical field of graph backdoor detection. The method comprises the following steps: performing node vector learning on graph data through a target graph neural network to obtain a target node vector corresponding to the graph data; performing clustering screening on the target node vector through a target clustering algorithm to determine an abnormal node in the graph data; performing structural disturbance on the abnormal node, calculating a target drift score of the abnormal node after the structural disturbance; generating a discrimination threshold based on the target drift score, and screening a trigger node in the graph data from the abnormal node according to the discrimination threshold. Through the cooperative mechanism of the representation learning to establish the discrimination basis, the clustering screening to narrow the search space, the structural disturbance to expose the stability difference, and the statistical modeling to automatically make decisions, the technical problem that it is difficult to accurately identify the hidden trigger node in the graph data is solved, and the stability and reliability of the detection process are improved.

The invention relates to a layout generation method and device, equipment, a storage medium and a program product. The layout generation method comprises the following steps: acquiring a target graph and target layout precision; determining a numerical value effective standard according to the target layout precision; and based on the numerical value effective standard and the geometric information of the target graph, generating a periodic arrangement graph of the target graph, and obtaining a target layout. According to the method and the device, the periodic arrangement graph with the target layout precision can be generated, so that advanced resolution enhancement technologies such as reverse photoetching technology correction and the like can be conveniently implemented on the full-chip layout.

The invention discloses a similar resource identification method and device, equipment, a medium and a program product, and relates to the technical field of computers.The method comprises the steps that target graph data corresponding to a resource recommendation platform is obtained, and the target graph data comprises nodes, edges between the nodes and weights of the edges; the types of the nodes comprise resource nodes, label nodes and resource acquisition object nodes, and resource acquisition objects comprise objects of which resources are acquired on the resource recommendation platform; walking in the target graph data based on the meta-path in the target graph data to obtain a node pair; processing node pairs in the target graph data to obtain features of the nodes; and determining the similarity among the resources in the resource recommendation platform based on the characteristics of the resource nodes to obtain similar resources. The similar resources determined by the method not only can be matched in the aspect of content, but also can be matched in the aspect of resource acquisition objects, so that the recognition accuracy of the similar resources can be improved.

The present application discloses a method and an apparatus for generating a textual description of graph data, a storage medium, an electronic device, and a program product. The method comprises: acquiring target graph data to be described; and converting the target graph data by using a pre-trained target graph-to-text model, so as to obtain a target textual description corresponding to the target graph data, wherein the target graph-to-text model is obtained by iteratively training an initial graph-to-text model by using multiple sets of sample data, each set of sample data comprises a graph data sample and a corresponding textual description label, and the textual description label is determined by a first textual description generated by a target entity describing the graph data sample and a second textual description obtained on the basis of centrality information of the graph data sample.

The application is suitable for the field of model explanation, and relates to a rule mining method and device for recommending model explanation, equipment and medium. For any user-item pair in graph data, a corresponding neighborhood graph and a connected subgraph are determined, a candidate subgraph is determined from the connected subgraph, a target subgraph is determined from the candidate subgraph according to a graph evaluation score, a target graph pattern is extracted from the target subgraph, for any variable in a pattern path of the target graph pattern, a target predicate is determined from all predicates corresponding to the variable, a candidate premise condition is formed by the variable and the target predicate, a candidate explanation rule is obtained according to the target graph pattern and a candidate premise condition set corresponding to all pattern paths in the target graph pattern, and a candidate explanation rule satisfying a preset condition is determined as a target explanation rule from all candidate explanation rules. The target explanation rule reflecting the prediction principle of the recommendation model is mined from the graph data as the global explanation of the recommendation model, and the effectiveness of the explanation is improved.

The invention relates to the technical field of data processing, in particular to a knowledge graph construction method and system for scientific and technological achievement management, and the method comprises the steps: obtaining an initial knowledge graph of each category of scientific and technological achievements, and according to the similarity between word vectors and structural feature vectors of a target entity and all entities in a reference graph, obtaining a knowledge graph of each category of scientific and technological achievements; obtaining a plurality of candidate entities of the target entity in the reference map, and screening out a plurality of to-be-aligned entities from all the candidate entities according to the difference between the attribute value sequence of the target entity and the predicted attribute value sequences of all the candidate entities, and according to the repetition condition of the target entity and each neighbor node in the to-be-aligned entity in the reference map, performing entity alignment between the target map and the reference map to obtain a fused knowledge map. According to the method, the accuracy of entity alignment between the knowledge maps constructed by different types of scientific and technological achievement data is improved.

The application provides a graph data processing method, system, device and medium, comprising processing a plurality of to-be-processed graph data in a training data set to obtain node features and edge features; generating a node feature matrix and a resistance variation array according to the node features and the edge features; generating first transition features according to a graph neural network, the node feature matrix and the resistance variation array; inputting the first transition features into one or more graph convolution layers in the graph neural network for weighted calculation to output second transition features; inputting the second transition features into a readout network previously constructed in the graph neural network to generate result features; adjusting weight parameters of the graph neural network according to the result features and a Hamiltonian loss function, realizing training of the graph neural network to obtain a target neural network; and generating target result features according to node features, edge features and the target graph neural network of target graph data. The graph data operation time is reduced, and the processing speed is accelerated.

Embodiments of the present application disclose a method, related device and equipment for entity recognition applied to natural language processing, which are used to improve the accuracy of entity recognition. The method of the embodiments of the present application comprises: obtaining a text to be recognized, obtaining corresponding syntactic relationship features and semantic features of each word to be recognized according to the text to be recognized, the syntactic relationship features comprising at least one of syntactic relationship features, part-of-speech combination relationship features and semantic dependency relationship features, obtaining corresponding fusion features of each word to be recognized according to the corresponding syntactic relationship features and semantic features of each word to be recognized, generating a basic graph network according to the fusion features of each word to be recognized and the fusion features of sample words, clustering the basic graph network according to a weight value to obtain a target graph network, the target graph network comprising a class set, and determining target entities belonging to an entity category in at least two words to be recognized according to the target graph network.

The invention discloses a trigger node detection method and device, equipment and a storage medium, and relates to the technical field of graph backdoor detection, and the method comprises the steps: carrying out the node vector learning of graph data through a target graph neural network, and obtaining a target node vector corresponding to the graph data; performing clustering screening on the target node vector through a target clustering algorithm, and determining abnormal nodes in the graph data; performing structural disturbance on the abnormal node, and calculating a target drift score of the abnormal node subjected to structural disturbance; and generating a distinguishing threshold value based on the target drift score, and screening trigger nodes in the graph data from the abnormal nodes according to the distinguishing threshold value. The technical problem that hidden trigger nodes in graph data are difficult to accurately recognize is solved through a collaborative mechanism of establishing a distinguishing basis through representation learning, reducing a search space through clustering screening, exposing stability difference through structural disturbance and performing statistical modeling automatic decision making, and the stability and reliability of the detection process are improved.

The present invention discloses a method and system for converting a relational model into a graph model based on an LLM model. The method includes the following steps: converting the relational model to be converted into an initial graph model based on the primary-foreign key mapping; iteratively optimizing the initial graph model based on the query requirements and various preset optimization strategies to obtain a target graph model. Among them, the method for optimizing the model based on the target optimization strategy is: enabling the LLM model to analyze the corresponding candidate optimization objects based on the corresponding first graph model, the query requirements, and the target optimization strategy, and generating corresponding analysis results based on the queried features corresponding to the optimizable objects; determining the corresponding target optimization objects based on the target optimization strategy and the analysis results, and performing optimization transformation based on the target optimization objects. The present invention can automatically complete the conversion from a relational model to a graph model and optimize the model according to query requirements, effectively improving query efficiency and storage utilization rate.

The invention provides a drawing operation method and device, equipment and a storage medium. Relates to the technical field of computers, in particular to the technical fields of drawing operation, graphic interaction and the like. According to the specific implementation scheme, a target drawing is displayed, the target drawing contains multiple existing primitives stacked in the depth direction, and the multiple existing primitives are in a visual state; in response to a selected operation executed on the target drawing, performing penetration processing on the target drawing in the depth direction based on the operation position of the selected operation on the screen and the operation frequency of the selected operation to obtain a target primitive selected by the selected operation; wherein each selection operation corresponds to one target primitive; and displaying the target primitive in a selected state.

The invention provides a ripple animation rendering method and device based on atlas data vertexes, and the method comprises the steps: creating an auxiliary thread, and creating an off-screen canvas in the auxiliary thread; preprocessing the off-screen canvas according to a target map, and enabling the off-screen canvas to correspond to the target map; performing multi-frame aperture rendering synthesis calculation in a preset area on the off-screen canvas to obtain ripple aperture rendering data of a corresponding frame number; and when the off-screen canvas completes the aperture rendering synthesis calculation of the preset frame number, sending the ripple aperture rendering data of the preset frame number to the main canvas for corresponding aperture rendering, thereby realizing the ripple aperture rendering of the preset area on the target map. By independently creating the auxiliary thread, rendering calculation and actual rendering are not executed through the same thread any more, so that the overall rendering performance is improved, and the method can cope with a scene with a relatively large data size.

The present application discloses an object recommendation method, apparatus, device, medium and product, relating to the field of artificial intelligence. The method includes: obtaining graph data corresponding to a plurality of candidate objects; inputting the graph data into a target graph neural network to obtain object node representations respectively corresponding to the plurality of candidate objects; in response to receiving an object recommendation request for a first object, determining a target object to be recommended to an account corresponding to the first object from second objects based on the correlation score between the first object node representation corresponding to the first object and a second object node representation; the target graph neural network is obtained by iteratively training a graph neural network, and in the iterative training process of the graph neural network, in each round of iterative training, first type of parameters are iteratively updated, and in the iterative training rounds meeting the round requirement, second type of parameters are iteratively updated. This method improves the training efficiency of the model, thereby ensuring the recommendation accuracy in downstream object recommendation.

The application discloses a kind of graph data query statement determination method, device, equipment and medium, it is related to computer technical field, including: obtaining graph data query information, and determine whether the graph data query information is legal;If the graph data query information is legal, then based on the graph data query information from local preset graph data query statement library filtering out graph data query statement;Obtain statement query configuration information, and based on the statement query configuration information from the graph data query statement determines out to be configured graph data query statement, and according to the statement query configuration information to the to be configured graph data query statement is configured, to obtain target graph data query statement.Through the above technical scheme of the application, the efficiency of graph data query statement determination can be improved, the diversity of business requirements is increased, and the security of graph data query statement is improved.

An inductive link prediction method based on subgraph and path comparative representation learning is characterized in that an overall knowledge graph comprises a plurality of structured triples et = (s, rT, o), surrounding subgraphs containing target nodes are extracted, isolated nodes and a complete neighbor relation are reserved, and node features of complete semantic information between neighbor nodes are obtained. And extracting a topological relation path between the head entity s and the tail entity o from the knowledge graph. Local structures and relation semantics are obtained through node-level relation coding, sub-graph-level aggregation perception message passing and an attention mechanism. And carrying out modeling on paths of adjacent relations of the sub-graphs to obtain features in the sub-graphs. A topological structure of a target map is associated with path representation, marginal-based loss is incorporated into distance scores of a positive path sample and a negative path sample, the positive path sample and the negative path sample which need to be distinguished are compared and learned through semantic information conveyed by a relation path, and a loss function is optimized through joint training.

This invention discloses a graph storage method for mixed query loads of transactional and analytical queries, belonging to the field of graph data storage. The method includes: storing graph data in a row-oriented storage structure before reaching the maximum capacity allocated in the LSM tree memory; when the written graph data reaches the maximum capacity, allocating new empty memory in the LSM tree memory to store the new graph data in a row-oriented storage structure, and converting the graph data in the old memory that has reached the maximum capacity into a column-oriented storage structure and migrating it to the LSM tree's external storage; in response to receiving mixed query concurrency requests, transactional query requests preferentially retrieve the first target graph data from the row-oriented storage structure of the LSM tree memory, while analytical query requests preferentially retrieve the second target graph data from the column-oriented storage structure of the LSM tree's external storage. This solution can reduce the response latency and computational resource consumption when handling mixed transactional / analytic queries.

The embodiment of the application provides a kind of based on multi-dimensional feature constraint's net body intelligent multi-model calling method and product, it is related to artificial intelligence technical field.The method comprises: obtaining directed graph structure;Wherein, directed graph structure includes multiple graph nodes and the node feature and the edge feature corresponding to each graph node;According to user task request, generate task request feature;According to task request feature and the multiple graph nodes in directed graph structure and the node feature and the edge feature corresponding to each graph node, one or more graph nodes in directed graph structure is determined as target graph node;According to target graph node, determine task processing path;Wherein, task processing path is used to call the model corresponding to target graph node according to the model calling order corresponding to task processing path when processing user task request;Solved the problem that the model called in prior art scheme exists poor matching, improves data processing efficiency and resource utilization.

The invention discloses a subgraph editing distance calculation method based on a graph neural network, and relates to the field of graph calculation and artificial intelligence, and the method comprises the following steps: obtaining a query graph and a target graph; generating node-level and edge-level representations for the query graph and the target graph through a unified graph isomorphism encoder to capture the influence of node and edge specific editing operation on graph topology; inputting the target graph into a self-adaptive graph mask module, generating mask scores of nodes and edges through a gating attention mechanism in combination with query graph representation, shielding irrelevant parts and dynamically balancing graph scale differences; and performing multi-head mask processing on the target map based on the mask score to generate a plurality of candidate substructures. According to the method, asymmetry can be effectively processed, fine-grained structure differences are captured, calculation precision and efficiency are improved, and the method is suitable for scenes such as drug discovery, social network analysis and recommendation systems.

The present invention provides a method, device, and medium for identity-preserving adversarial training based on graph representation learning. The method comprises: obtaining graph data of a training scenario, defining each node of the graph data as an original sample used to represent the training scenario, and defining the sample identity information of the original sample; generating an adversarial sample corresponding to each original sample; adding an identity-preserving constraint to the adversarial sample so that the adversarial sample retains the sample identity information of the original sample; inputting the adversarial sample as the first input variable into an initial graph representation learning model and performing identity-preserving adversarial training; updating the initial graph representation learning model to obtain a target graph representation learning model, and using the target graph representation learning model to predict the output of the original sample in the training scenario under different graph mining tasks. This method maintains the same sample identity information between the adversarial sample and the original sample, improving the accuracy of graph representation learning in graph structure data analysis and having a certain degree of universality.

The invention provides a code completion method, electronic equipment, a storage medium and a program product. The code completion method comprises the following steps: in response to a code completion request, determining a target code entity corresponding to a cursor position in a code editing interface; target graph nodes corresponding to the target code entities are recognized from the dependency graph, graph nodes in the dependency graph are used for representing the code entities, and edges are used for representing dependency relations between the code entities; cutting out adjacent sub-graphs related to target graph nodes from the dependency graph; according to the code snippets corresponding to the graph nodes in the adjacent sub-graphs and the code context of the cursor position, constructing cue words, taking the cue words as input of a large language model, and outputting codes to complement the text by the large language model; and displaying the code complementation text in the code editing interface.

The present application is applicable to the technical field of big data mining, and particularly relates to a cross-dataset graph association analysis method and apparatus, and a medium. The method comprises: determining matching pairs in external graph data and target graph data; acquiring a first path of any matching pair between a first vertex and a second vertex in the external graph data, and calculating a first ranking score representing the semantic importance of the first path in the external graph data; acquiring a second path in the target graph data that uses the first vertex as a starting point and has a secondary intersection with the first path, and calculating a second ranking score representing the semantic importance of the second path in the target graph data; and filtering the external graph data on the basis of the first ranking score and the second ranking score to obtain filtered external graph data, and performing association rule mining on the basis of the filtered external graph data and the target graph data to obtain a rule mining result. While reducing noise and errors, the present application expands the scope of rule discovery for target graph data, thereby improving the accuracy of graph association analysis.

The specification provides an optimization method, device, storage medium and equipment of a graph computing execution plan. In the method, an optimization rule is obtained; attribute information of a target graph operator in an initial graph computing execution plan is detected, if the attribute information of the target graph operator is detected to match the optimization rule, the target graph operator is optimized based on the optimization rule to obtain an optimized graph operator, and attribute information of the optimized graph operator and attribute information of other graph operators associated with the optimized graph operator in the initial graph computing execution plan are updated; and an optimized graph computing execution plan is determined according to each optimized graph operator. The scheme realizes effective optimization of the graph computing execution plan, and further guarantees performance improvement of the optimized graph computing execution plan.

The present invention discloses a text classification method, device, computer equipment and storage medium, the method comprising: obtaining a document to be classified, and segmenting the document; constructing a graph network containing four types of nodes, and setting a corresponding graph network matrix, wherein the four types of nodes refer to document nodes, paragraph nodes, sentence nodes and word nodes; constructing a target adjacency matrix; performing subgraph clustering on the four types of nodes, and combining the graph network matrix to obtain a target graph network matrix; inputting the target adjacency matrix and the target graph network matrix into a graph convolutional neural network, and the graph convolutional neural network outputs a graph latent vector corresponding to the document, and then using a classification function to classify the graph latent vector to obtain a classification result of the document. The embodiment of the present invention uses a graph convolutional neural network to extract deep semantic features in a document, so that text classification is more accurate.

The embodiment of the invention provides a task scheduling method and device for a graph processing request. The method comprises the steps of obtaining a plurality of task units corresponding to a target image processing request; the plurality of task units correspond to a plurality of calculation modes; obtaining a state machine migration graph which is uniformly constructed for multiple calculation modes; the state machine migration graph comprises a plurality of nodes, each node indicates an execution state of a subtask split from each calculation mode, and a migration edge between the nodes corresponds to the subtask; according to the state machine migration graph, the task units are scheduled and executed, and the scheduling execution of any target task unit comprises the steps that in a first state indicated by the first node, in response to a first triggering condition for starting to migrate to a second state, a target job in the target task unit is scheduled and executed, therefore, the current state is transferred to the second state; the target job belongs to a target subtask corresponding to a migration edge between the first node and a second node corresponding to the second state.