A method and apparatus for determining node influence

Abstract

The application discloses a method and device for determining node influence, relates to the technical field of network analysis, and is used for improving the accuracy of determining user influence, so as to improve the efficiency of resource allocation and the efficiency of information transmission in a network. The method comprises the following steps: acquiring access information of a plurality of nodes in the network; wherein the access information is information of accessing the network; determining a second node adjacent to a first node and a third node connected to the first node in the network according to the similarity between the access information of the plurality of nodes; wherein the plurality of nodes comprise the first node, the second node and the third node; determining local influence of the first node according to the similarity between the access information of the first node and the access information of the second node; determining global influence of the first node according to the similarity between the access information of the first node and the access information of the third node; and determining influence of the first node in the network according to the local influence and the global influence.

Description

Technical Field

[0001] This application relates to the field of network analysis technology, and in particular to a method and apparatus for determining node influence. Background Technology

[0002] The current development of the Internet has accelerated the sharing of information and the establishment of large-scale complex networks. Each node in the network plays a crucial role in the dissemination of messages. By determining the influence of nodes in the network, resources can be allocated rationally according to different levels of influence in order to control the spread of messages.

[0003] Current research on node influence in complex networks generally focuses on three aspects: network topology analysis, network content analysis, or node behavior analysis. However, single-dimensional analysis methods are biased and fail to consider important factors in other dimensions. Therefore, a comprehensive three-dimensional approach is needed to determine node influence. Summary of the Invention

[0004] This application provides a method and apparatus for determining node influence, which improves the accuracy of determining user influence, thereby improving the efficiency of resource allocation and information dissemination in the network.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0006] In a first aspect, a method for determining node influence is provided, comprising: acquiring access information of multiple nodes in a network; wherein the access information is information about accessing the network; determining a second node adjacent to a first node and a third node connected to the first node in the network based on the similarity between the access information of the multiple nodes; wherein the multiple nodes include the first node, the second node, and the third node; determining the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node; determining the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node; and determining the influence of the first node in the network based on the local influence and the global influence.

[0007] The method provided in the first aspect involves a computer device determining the local and global influence of any node in the network by analyzing the similarity between information accessed by nodes in the network. Local influence refers to the influence a node has on its neighboring nodes, while global influence refers to the influence a node has on nodes with which it has connections. This method helps to rationally allocate resources based on the influence of different nodes and improve the efficiency of information dissemination.

[0008] In one possible implementation, a network model is established based on access information from multiple nodes. This network model includes identifiers of the multiple nodes, connection relationships between the nodes, and weights of these connection relationships. The weight of the connection relationship between any two nodes is used to characterize the similarity between the access information of the two nodes. The local influence of a first node is determined based on the similarity between its access information and that of a second node. This includes: obtaining a first weight from the network model to determine the local influence of the first node based on the first weight; and / or, the global influence of a first node is determined based on the similarity between its access information and that of a third node. This includes: obtaining a second weight from the network model to determine the connection relationship between the first node and the third node, and the connection relationship between the first node and the third node; and determining the global influence of the first node in the network based on the second weight and the connection relationship between the first node and the third node.

[0009] This possible implementation provides a specific way to determine node influence. By establishing a network model, the influence of a node is determined based on the connection relationships and weights between nodes in the network model. This helps in subsequent model training and improves the accuracy of determining node influence.

[0010] In one possible implementation, determining the influence of the first node in the network based on the local influence and the global influence includes: determining the category of the influence of multiple nodes by a clustering algorithm based on the local influence and the global influence of the multiple nodes; and determining the category of the influence of the first node based on the category of the influence of the multiple nodes.

[0011] This possible implementation method uses clustering algorithms to categorize nodes based on their influence, which helps to allocate resources according to categories.

[0012] In one possible implementation, the influence categories of multiple nodes are determined by a clustering algorithm based on their local and global influence. This includes: marking the local and global influence of multiple nodes in a two-dimensional coordinate system; wherein the two-dimensional coordinate system is a two-dimensional coordinate system composed of local and global influence; and determining the influence categories of multiple nodes by a clustering algorithm based on the two-dimensional coordinate system.

[0013] One possible implementation method is to determine the influence of nodes based on clustering algorithms. This method can help classify the influence of nodes and is simple and convenient to implement.

[0014] In a second aspect, a computer device is provided, comprising: functional units for performing any of the methods provided in the first aspect, wherein the actions performed by each functional unit are implemented by hardware or by hardware executing corresponding software. For example, the computer device may include: an acquisition unit and a determination unit; the acquisition unit is configured to acquire access information of multiple nodes in a network; wherein the access information is information about accessing the network; the determination unit is configured to determine, based on the similarity between the access information of the multiple nodes, a second node adjacent to a first node and a third node connected to the first node in the network; wherein the multiple nodes include the first node, the second node, and the third node; the determination unit is further configured to determine the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node; the determination unit is further configured to determine the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node; and the determination unit is further configured to determine the influence of the first node in the network based on the local influence and the global influence.

[0015] Thirdly, a computer device is provided, comprising: a processor and a memory. The processor is connected to the memory, the memory being used to store computer execution instructions, and the processor executing the computer execution instructions stored in the memory, thereby implementing any of the methods provided in the first aspect.

[0016] Fourthly, a chip is provided, comprising: a processor and an interface circuit; the interface circuit for receiving code instructions and transmitting them to the processor; and the processor for executing the code instructions to perform any of the methods provided in the first aspect.

[0017] Fifthly, a computer-readable storage medium is provided, including computer-executable instructions that, when executed on a computer, cause the computer to perform any of the methods provided in the first aspect.

[0018] In a sixth aspect, a computer program product is provided, including computer execution instructions that, when executed on a computer, cause the computer to perform any of the methods provided in the first aspect.

[0019] The technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here. Attached Figure Description

[0020] Figure 1 A schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0021] Figure 2 A flowchart illustrating a method for determining node influence provided in an embodiment of this application;

[0022] Figure 3 A flowchart illustrating a method for determining node influence provided in an embodiment of this application;

[0023] Figure 4 A two-dimensional coordinate diagram of node influence provided in an embodiment of this application;

[0024] Figure 5 A schematic diagram of clustering results for determining node influence based on a two-dimensional coordinate system, provided as an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of the composition of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0027] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] This application provides a method for determining node influence, applicable to computer devices. This application does not limit the specific form of the computer device. For example, the computer device can be a terminal device or a network device. The terminal device can be referred to as: terminal, user equipment (UE), terminal device, access terminal, user unit, user station, mobile station, remote station, remote terminal, mobile device, user terminal, wireless communication device, user agent, or user equipment, etc. The terminal device can specifically be a mobile phone, augmented reality (AR) device, virtual reality (VR) device, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. The network device can specifically be a server, base station, etc. The server can be a single physical or logical server, or two or more physical or logical servers sharing different responsibilities and cooperating to achieve the various functions of the server.

[0029] In terms of hardware implementation, the aforementioned computer equipment can be implemented through, for example... Figure 1 The computer device shown is implemented as follows. Figure 1 The diagram shown is a hardware structure schematic of a computer device 10 provided in an embodiment of this application. The computer device 10 can be used to implement the functions of the aforementioned computer device.

[0030] Figure 1 The computer device 10 shown may include a processor 101, a memory 102, a communication interface 103, and a bus 104. The processor 101, the memory 102, and the communication interface 103 can be connected via the bus 104.

[0031] The processor 101 is the control center of the computer device 10. It can be a general-purpose central processing unit (CPU) or other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0032] As an example, processor 101 may include one or more CPUs, for example Figure 1 CPU 0 and CPU 1 are shown in the diagram.

[0033] The memory 102 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0034] In one possible implementation, the memory 102 may exist independently of the processor 101. The memory 102 can be connected to the processor 101 via a bus 104 and is used to store data, instructions, or program code. When the processor 101 calls and executes the instructions or program code stored in the memory 102, it can implement the method for determining node influence provided in the embodiments of this application.

[0035] In another possible implementation, the memory 102 can also be integrated with the processor 101.

[0036] The communication interface 103 is used for connecting the computer device 10 to other devices via a communication network, which may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 103 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0037] Bus 104 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 1 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0038] It should be pointed out that, Figure 1 The structure shown does not constitute a limitation on computer device 10, except... Figure 1In addition to the components shown, the computer device 10 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0039] To make the embodiments of this application clearer, the following is a brief introduction to the concepts and some contents related to the embodiments of this application.

[0040] 1. Network

[0041] A network is composed of numerous nodes and the connections between them. The network in this application example can be a social network comprised of users' real-world social relationships. A network can also be a network composed of relationships between any objects (including people and things), such as the Internet of Things (IoT).

[0042] 2. Nodes

[0043] In a network, a node refers to a device node or a user node. For example, a device node can be a workstation, personal computer, server, printer, or other similar device. A user node can be a client or network user.

[0044] 3. Online resources

[0045] Network resources mainly refer to the sum of various information resources that can be utilized through the network environment. Network resources can also be understood as resources within the network, information within the network, or information resources within the network.

[0046] Device nodes can access the network and obtain information resources within the network, and the network can identify a device node through its access address; user nodes can access network information resources by logging into a device that has accessed the network, and the network can identify a user node through its login username.

[0047] like Figure 2 The diagram shown is a flowchart of a method for determining node influence provided in this application. The method includes:

[0048] S201. The computer device obtains access information from multiple nodes in the network.

[0049] Access information refers to information about accessing the network. Access information includes at least one of the following: address information of accessed network resources, access time information, access traffic information, etc.

[0050] Optionally, in the World Wide Web (WWW), every information resource has a unique address on the network, which is called a Uniform Resource Locator (URL). Nodes access resources on the network through URL information, which can serve as access information for nodes.

[0051] Optionally, the access time information may include the node's entry and exit times when accessing the network, or it may include the duration of the node's access to the network.

[0052] Optionally, access traffic information can be uplink and / or downlink traffic information during a node's network access process. Uplink traffic information refers to the amount of information resources a node sends to the network during its network access process, while downlink traffic information refers to the amount of information resources a node downloads from the network during its network access process.

[0053] Optionally, the computer device can first obtain the node type of any node in the network, and then obtain the node's access information based on that node type. Node type refers to the type of device used by the node to access the network. For example, a user node can access the internet through different terminals; in this case, the node type can be the type of terminal, such as a mobile phone model, laptop model, etc. The computer device can determine the different influences between different node types by combining the node type and the node's access information.

[0054] Optionally, the computer device can acquire mobile internet data from a node in the network to obtain the node's access information. This mobile internet data may include the node's identifier and its access information.

[0055] It should be noted that this mobile internet data can be raw mobile internet data, meaning that in addition to the node identifier and access information, it may also include other data information. When a computer device obtains this raw mobile internet data, it can filter the information to obtain mobile internet data containing the node identifier and at least one of the aforementioned access information.

[0056] One possible implementation is that the node's mobile internet data can be stored in the base station, and computer devices obtain the node's mobile internet data by interacting with the base station. Currently, nodes in the network typically access the network and exchange information through base stations. The information exchanged by nodes in the network can be stored in the base station during transmission.

[0057] It should be noted that the network includes multiple nodes, which can access the network through different base stations. Therefore, computer devices can obtain mobile Internet data from multiple nodes through different base stations.

[0058] Another possible implementation is that the node's mobile internet data can be stored in other network devices, and the computer device can obtain the mobile internet data from other network devices.

[0059] S202. The computer device determines a second node adjacent to the first node and a third node connected to the first node in the network based on the similarity between the access information of multiple nodes; the multiple nodes include the first node, the second node and the third node, and the first node is any one of the multiple nodes.

[0060] Computer devices can represent the similarity between access information of any two nodes from a set of multiple nodes by calculating the similarity between their access information. The similarity between two nodes indicates the degree to which their access information matches. Similarity algorithms include cosine similarity, etc.

[0061] This paper uses the URL information accessed by nodes as access information and cosine similarity to represent the similarity of access information between nodes as an example. URL information generally includes the resource type, the host domain name storing the resource, and the resource file name. Based on resource type, URL information is divided into type 1, type 2, type 3, and type 4. Assume node A accessed URL information of type 1 and type 2, node B accessed URL information of type 2 and type 3, node C accessed type 1, and node D accessed type 4. The number of times nodes A, B, C, and D accessed different types of URL information is shown in Table 1 below.

[0062] Table 1. Number of times different nodes accessed different types of URL information

[0063] Type 1 Type 2 Type 3 Type 4 Node A 1 2 0 0 Node B 0 5 6 0 Node C 4 0 0 0 Node D 0 0 0 1

[0064] As shown in Table 1, both node A and node B accessed type 2, and both node A and node C accessed type 1. Therefore, there is similarity between node A and node B, and between node A and node C. Only node D accessed type 4, so there is no similarity between node A, node B, node C, and node D. Node B and node C accessed different types of URL information; therefore, there is also no similarity between node B and node C.

[0065] Represent the number of visits to node A, node B, and node C and the type of access URL information as vectors A, B, and C, respectively, and calculate the cosine similarity between any two vectors.

[0066] Based on the aforementioned similarity assessment, the computer device determines the connection relationship with the first node, which can be a direct connection or an indirect connection. Nodes with a similarity greater than a first threshold to the first node are directly connected nodes, i.e., second nodes adjacent to the first node, also known as neighboring nodes of the first node. The computer device can set the first threshold based on the precision of the similarity assessment. Optionally, the first threshold can be 0.

[0067] These multiple nodes also include a third node connected to the first node. Connecting to the third node means having a direct or indirect connection with the first node.

[0068] Optionally, nodes indirectly connected to the first node can be determined through second nodes. Specifically, the computer device identifies nodes whose access information similarity to the second node is greater than a first threshold as nodes directly connected to the second node, i.e., nodes indirectly connected to the first node. This process continues, with the computer device identifying nodes directly connected to the first node, until the condition for a direct connection is no longer met or all nodes in the network have completed their determinations, thus identifying all nodes indirectly connected to the first node. Nodes indirectly connected to the first node, along with the second node, are collectively referred to as third nodes. The second node is included in the third node list.

[0069] For example, if node D is directly connected to node E, and node D is directly connected to node F, then node E and node F are indirectly connected through node D. Node D is adjacent to node E, and node D is adjacent to node F. Any two of nodes D, E, and F are connected. When the first node is node E, the second node includes node D, and the third node includes nodes D and F.

[0070] Optionally, the computer device determines a third node connected to the first node, including determining the distance between the third node and the first node. The connection between two nodes can be represented by the number of hops between the nodes in the path between the two nodes. Taking nodes D, E, and F as an example again, the distance between node E and node F is 2 hops.

[0071] It should be noted that the path between the first node and the third node can include one or more paths, and the number of hops between nodes in different paths can be the same or different.

[0072] S203. The computer device determines the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node.

[0073] The local influence of a first node refers to the influence that the first node exerts on a second node. Specifically, a first node has one or more adjacent second nodes.

[0074] Optionally, the computer device can sum the similarity between the access information of the first node and the access information of each second node to determine the local influence of the first node. For example, the computer device uses the summation result as the local influence of the first node.

[0075] S204. The computer device determines the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node.

[0076] The global influence of a first node refers to the influence that the first node exerts on third nodes. Here, the first node has one or more third nodes connected to it. It can be understood that, since the third nodes include second nodes, the global influence of the first node is a combination of its local influence and its influence on nodes indirectly connected to it.

[0077] Optionally, the computer device can determine the global influence of the first node by calculating the similarity between the access information of the first node and the access information of the third node, and combining this with the distance between the first node and the third node. Specifically, the computer device determines the shortest distance between the first node and the third node obtained in step S202 as a weighting coefficient for calculating the similarity between the access information of the first node and the third node, then calculates the weighted sum of the similarities between the access information of the first node and the access information of the third node, and uses this weighted sum as the global influence of the first node.

[0078] The execution order of steps S203 and S204 is not limited in this embodiment.

[0079] S205. The computer equipment determines the influence of the first node in the network based on the local influence and the global influence of the first node.

[0080] Optionally, S205 may include S205A-S205B:

[0081] S205A: The computer equipment marks the local and global influence of these multiple nodes in a two-dimensional coordinate system; where the two-dimensional coordinate system is composed of the local and global influences. For example... Figure 4 As shown, with local influence as the horizontal axis and global influence as the vertical axis, the computer device uses a two-dimensional coordinate system (LI) based on the local and global influence of each of the multiple nodes. i GI i Generate marker points in the coordinate system.

[0082] S205B: The computer device determines the category of influence of the multiple nodes through a clustering algorithm, and determines the category of influence of the first node based on the category of influence of the multiple nodes.

[0083] Clustering algorithms refer to the clustering analysis of datasets. Cluster analysis consists of several patterns. This application considers both the local and global influence of nodes, and therefore represents them as point patterns in a multi-dimensional space. Cluster analysis is based on similarity; patterns within a cluster are more similar than patterns in different clusters.

[0084] Clustering algorithms include various methods such as partitioning and hierarchical methods. Partitioning methods group records in a dataset into categories, with each group representing a class. Records within a group are considered similar, while records in different groups are considered as far apart or different as possible. Common partitioning algorithms include K-MEANS, K-MEDOIDS, and CLARANS. Hierarchical methods decompose records in a dataset layer by layer according to certain conditions until a specific condition is met. Hierarchical methods can be further divided into bottom-up and top-down approaches.

[0085] Computer devices perform clustering based on marker points in a two-dimensional coordinate system, and then use the clustering results as the category of node influence. For example, suppose the clustering result is as follows: Figure 5 As shown, there are four categories: A, B, C, and D. It can be seen that nodes within the same category are close to each other, while nodes in different categories are far apart. Specifically, category A consists of nodes with high global influence and low local influence. Category B consists of nodes with low global influence and low local influence. Category C consists of nodes with low global influence and high local influence. Category D consists of nodes with high global influence and high local influence. Based on this example, the influence of multiple nodes is specifically categorized into categories A, B, C, and D.

[0086] Optionally, different categories have different physical meanings. Computer devices can define nodes with small global influence and small local influence as "ordinary nodes", nodes with small global influence and large local influence as "propagation nodes", nodes with large global influence and small local influence as "influencer nodes", and nodes with large global influence and large local influence as "leader nodes".

[0087] Optionally, nodes that do not belong to any category can have their physical meaning defined separately. For example... Figure 5 In this context, for discrete nodes that are not included in any of the categories A through D, a computer device can define a "local node" separately for its local and global influence.

[0088] Computer devices use clustering algorithms to determine the influence categories of multiple nodes. Analysis shows that nodes grouped into a particular category after clustering have similar global and local influence. For each category of nodes, network resources can be allocated strategically based on the influence characteristics of that category, effectively controlling information propagation. For example, if a node has a large local influence but a small global influence within a certain area, its information propagation efficiency is high within that area, while its influence is relatively small over larger areas.

[0089] The above steps S203-S204 can be specifically implemented through the following steps S301-S303:

[0090] S301. The computer equipment establishes a network model based on the access information of multiple nodes.

[0091] The network model includes identifiers for multiple nodes, connections between these nodes, and weights for those connections. The weights of the connections between any two nodes are used to characterize the similarity between the access information of the two nodes.

[0092] Multiple nodes can be identified by V, where V = {V1, V2, ..., V...} i}, where i represents the node identifier. The connection between multiple nodes can be called an edge, which can be represented by E, where E = {E1, E2, ..., E}. m}, where m represents the number of edges. The weight of the connection between multiple nodes can be called the edge weight, which can be represented by W, where the edge weight between node i and node j is W. ij It can be calculated using formula (1):

[0093] W ij =s ij ,j∈[1,2,…,k]; (1)

[0094] Where k is the number of nodes adjacent to node i, s ij This represents the similarity between node i and the second node j.

[0095] Based on the above, the network model can be represented by G, where G = {V, E, W}.

[0096] S302. The computer device obtains the first weight of the connection relationship between the first node and the second node from the network model, and determines the local influence of the first node based on the first weight.

[0097] Specifically, the computer device calculates a first weight for the connection relationship between one or more second nodes adjacent to the first node and each of the first nodes. The computer device sums up the one or more first weights and uses the sum as the local influence of the first node, as shown in formula (2).

[0098] LI i =ΣW ij ,j∈[1,2,…,k]; (2)

[0099] Among them, LI i W represents the local influence of the first node i. ij The first weight is the connection relationship between the first node i and the second node j.

[0100] S303. The computer device obtains the second weight of the connection relationship between the first node and the third node, as well as the connection relationship between the first node and the third node, from the network model; based on the second weight and the connection relationship between the first node and the third node, it determines the global influence of the first node in the network.

[0101] Optionally, if there are multiple paths between the first node and the third node, the computer device can first determine the shortest path d between the first node and the third node from among these multiple paths based on the connection relationship between them; then, it calculates the similarity between the first node and the third node in path d, and uses this similarity as the second weight of the connection relationship between the first node and the third node. Wherein, the edge weight W between node i and node t... it It can be calculated using formula (3):

[0102] W it =s' it ,t∈[1,2,…,j,j+1,…,o]; (3)

[0103] W it Let s' be the second weight between the first node i and the third node t. it represents the similarity between the first node i and the third node t, and o represents the number of third nodes t.

[0104] Considering that there are multiple paths between the first node and the third node, and since the similarity between two nodes generally decreases as the distance between them increases, the computer determines that the access information between the first node and the third node has the highest similarity based on the shortest path d between them. This is beneficial for determining the maximum global influence of the first node.

[0105] For example, a computer device can determine the similarity between the first node and the third node within the shortest path d between the first node and the third node based on formula (4).

[0106] s' it =Σs pq , p∈[i, i+1,…,t-1], q=p+1; (4)

[0107] p is the identifier of any node between the first node i and the third node t in the shortest path d, q is the identifier of the node directly connected to node p, and s pq Let s be the similarity between the behavioral preference information of nodes p and q. pq Summing is performed to obtain the sum of the similarities between all nodes that have direct connections to the first node and the third node within the shortest path d. This sum is then used as the second weight of the connection between the first node and the third node.

[0108] Optionally, when there are multiple paths with the shortest distance d between the first node and the third node, the computer device obtains the path s' with the same shortest distance d. it The maximum value (max(s') it )), the max(s' it This serves as the second weight for the connection between the first node and the third node.

[0109] If multiple third nodes exist connected to the first node, the computer device calculates the second weights of the connection relationships between each of the multiple third nodes and the first node based on these multiple third nodes connected to the first node. Considering the different distances between the first node and the multiple third nodes, these different distances are used as weighting coefficients, and the weighted sum of the multiple second weights is calculated. The result is taken as the global influence of the first node. As shown in the following formula (5).

[0110] GI i =ΣS(d it )*W it ,t∈[1,2,…,j,j+1,…,o]; (5)

[0111] GI i Let d be the global influence of the first node i. it Let be the shortest distance between the first node i and the third node t.

[0112] Optionally, in the above formula (5), for ease of calculation, the shortest distance d can be used. it Normalization is performed using sigmoid functions. Sigmoid functions include the sigmoid function, S(d it ) represents the shortest path d it The normalized result.

[0113] It should be noted that since the first node and the second node are directly connected, their distance is assumed to be 1 hop. When calculating the similarity of access information between the first and second nodes, the computer can ignore the distance factor.

[0114] Optionally, the execution order of steps S302 and S303 is not important.

[0115] By establishing the network model as described above, we can determine the edges and weights of each node based on the multiple nodes in the network, which facilitates subsequent model training and improves the accuracy of determining the influence of nodes.

[0116] It should be noted that this application is not limited to determining the local or global influence of a node by establishing a network model and then training the model; it can also be determined by other methods.

[0117] The foregoing primarily describes the solutions of the embodiments of this application from a methodological perspective. It is understood that, in order to achieve the above-described functions, the computer device includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] This application embodiment can divide a computer device into functional units based on the above method examples. For example, each function can be divided into its own functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0119] For example, Figure 6 A possible structural schematic diagram of the computer device (denoted as computer device 50) involved in the above embodiments is shown. The computer device 60 includes an acquisition unit 601 and a determination unit 602.

[0120] The acquisition unit 601 is used to acquire access information of multiple nodes in the network; wherein, the access information is information about accessing the network; for example, Figure 2The step S201 is shown.

[0121] The determining unit 602 is used to determine, based on the similarity between the access information of the plurality of nodes, a second node adjacent to the first node and a third node connected to the first node in the network; wherein the plurality of nodes includes the first node, the second node and the third node;

[0122] The determining unit 602 is also used to determine the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node.

[0123] The determining unit 602 is also used to determine the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node.

[0124] Unit 602 is further configured to determine the influence of the first node in the network based on the local influence and the global influence. For example, Figure 2 The steps S202-S205 shown, and Figure 3 The steps S301-S303 are shown.

[0125] Optionally, a creation unit is used to establish a network model based on the access information of multiple nodes; wherein, the network model includes the identifiers of multiple nodes, the connection relationships between multiple nodes, and the weights of the connection relationships between multiple nodes, and the weights of the connection relationships between any two nodes are used to characterize the similarity between the access information of the two nodes; the determination unit 602 is further used to obtain the first weight of the connection relationship between the first node and the second node from the network model, and determine the local influence of the first node based on the first weight; and / or, the determination unit 602 is further used to obtain the second weight of the connection relationship between the first node and the third node from the network model, and the connection relationship between the first node and the third node; and determine the global influence of the first node in the network based on the second weight and the connection relationship between the first node and the third node.

[0126] Optionally, the determining unit 602 is specifically used to determine the category of the influence of multiple nodes by using a clustering algorithm based on the local influence and global influence of multiple nodes; and to determine the category of the influence of the first node based on the category of the influence of multiple nodes.

[0127] Optionally, the determining unit 602 is specifically used to mark the local influence and global influence of multiple nodes in a two-dimensional coordinate system; wherein, the two-dimensional coordinate system is a two-dimensional coordinate system composed of local influence and global influence; based on the two-dimensional coordinate system, the category of influence of multiple nodes is determined by a clustering algorithm.

[0128] Optionally, the computer device 60 also includes a storage unit 603. The storage unit 603 is used to store computer execution instructions, and other units in the computer device can perform corresponding actions according to the computer execution instructions stored in the storage unit 603.

[0129] For a detailed description of the above-mentioned optional methods, please refer to the foregoing method embodiments, which will not be repeated here. Furthermore, the explanation of any of the computer devices 60 provided above and the description of their beneficial effects can be found in the corresponding method embodiments described above, and will not be repeated here.

[0130] As an example, combined Figure 1 The functions implemented by some or all of the acquisition unit 601, determination unit 602, and storage unit 603 in computer device 60 can be achieved through... Figure 1 Processor 101 in the middle executes Figure 1 The program code in memory 102 is used for implementation. The acquisition unit 601 can also be implemented through... Figure 1 The receiving unit in the communication interface 103 is implemented.

[0131] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the methods executed by any of the computer devices described above.

[0132] For explanations of the relevant content and descriptions of the beneficial effects in any of the computer-readable storage media provided above, please refer to the corresponding embodiments described above, which will not be repeated here.

[0133] This application also provides a chip. The chip integrates a control circuit for implementing the functions of the aforementioned computer device 60 and one or more ports. Optionally, the functions supported by the chip can be referred to above, and will not be repeated here. Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, random access memory, etc. The aforementioned processing unit or processor can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (digital signal processor, DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0134] This application also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or may include one or more data storage devices such as servers or data centers that can be integrated with the medium. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0135] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of this application, such as but not limited to the memory, computer-readable storage medium and communication chip, are all non-transitory.

[0136] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, SSDs).

[0137] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0138] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for determining node influence, characterized in that, include: Obtain access information from multiple nodes in a network; wherein the access information is information about accessing the network; the access information includes at least one of address information, access time information, and access traffic information for accessing network resources; Based on the similarity between the access information of the plurality of nodes, a second node adjacent to the first node and a third node connected to the first node are determined in the network; wherein, the plurality of nodes include the first node, the second node and the third node; The local influence of the first node is determined based on the similarity between the access information of the first node and the access information of the second node. The global influence of the first node is determined based on the similarity between the access information of the first node and the access information of the third node. The local and global influence of the multiple nodes are marked in a two-dimensional coordinate system; wherein, the two-dimensional coordinate system is a two-dimensional coordinate system composed of local and global influence. Based on the two-dimensional coordinate system, a clustering algorithm is used to determine the category of influence of the multiple nodes; The category of influence of the first node is determined based on the category of influence of the plurality of nodes.

2. The method according to claim 1, characterized in that, The method further includes: A network model is established based on the access information of the multiple nodes; wherein, the network model includes the identifiers of the multiple nodes, the connection relationships between the multiple nodes, and the weights of the connection relationships between the multiple nodes, and the weights of the connection relationships between any two nodes are used to characterize the similarity between the access information of the two nodes. The step of determining the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node includes: obtaining a first weight of the connection relationship between the first node and the second node from the network model, and determining the local influence of the first node based on the first weight. And / or, determining the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node includes: obtaining a second weight of the connection relationship between the first node and the third node from the network model, and the connection relationship between the first node and the third node; and determining the global influence of the first node in the network based on the second weight and the connection relationship between the first node and the third node.

3. A computer device, characterized in that, include: An acquisition unit is used to acquire access information of multiple nodes in a network; wherein the access information is information about accessing the network; the access information includes at least one of address information, access time information, and access traffic information for accessing network resources; A determining unit is configured to determine, based on the similarity between the access information of the plurality of nodes, a second node adjacent to the first node and a third node connected to the first node in the network; wherein the plurality of nodes includes the first node, the second node, and the third node; The determining unit is further configured to determine the local influence of the first node based on the similarity between the access information of the first node and the access information of the second node. The determining unit is further configured to determine the global influence of the first node based on the similarity between the access information of the first node and the access information of the third node. The determining unit is further configured to mark the local influence and global influence of the plurality of nodes in a two-dimensional coordinate system; wherein, the two-dimensional coordinate system is a two-dimensional coordinate system composed of local influence and global influence; and based on the two-dimensional coordinate system, to determine the category of the influence of the plurality of nodes through a clustering algorithm. The determining unit is further configured to determine the category of the influence of the first node based on the category of the influence of the plurality of nodes.

4. The computer device according to claim 3, characterized in that, The computer device also includes: A creation unit is used to establish a network model based on the access information of the multiple nodes; wherein, the network model includes the identifiers of the multiple nodes, the connection relationships between the multiple nodes, and the weights of the connection relationships between the multiple nodes, and the weights of the connection relationships between any two nodes are used to characterize the similarity between the access information of the two nodes. The determining unit is further configured to obtain a first weight of the connection relationship between the first node and the second node from the network model, and determine the local influence of the first node based on the first weight; And / or, the determining unit is further configured to obtain, from the network model, a second weight of the connection relationship between the first node and the third node, and the connection relationship between the first node and the third node; and based on the second weight and the connection relationship between the first node and the third node, determine the global influence of the first node in the network.

5. A computer device, characterized in that, include: processor; The processor is connected to a memory for storing computer execution instructions, and the processor executes the computer execution instructions stored in the memory to enable the computer device to implement the method as described in any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, Used to store computer instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1-2.

7. A computer program product, characterized in that, It includes computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-2.

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