WO2022143505A1 - 群组类型识别方法、装置、计算机设备及介质 - Google Patents

群组类型识别方法、装置、计算机设备及介质 Download PDF

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Publication number
WO2022143505A1
WO2022143505A1 PCT/CN2021/141553 CN2021141553W WO2022143505A1 WO 2022143505 A1 WO2022143505 A1 WO 2022143505A1 CN 2021141553 W CN2021141553 W CN 2021141553W WO 2022143505 A1 WO2022143505 A1 WO 2022143505A1
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Prior art keywords
user
user nodes
feature
nodes
graph
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PCT/CN2021/141553
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English (en)
French (fr)
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陈昊
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腾讯科技（深圳）有限公司
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Priority to JP2023519589A priority Critical patent/JP2023544022A/ja
Publication of WO2022143505A1 publication Critical patent/WO2022143505A1/zh
Priority to US17/963,919 priority patent/US11916853B2/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/04—Real-time or near real-time messaging, e.g. instant messaging [IM]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/185—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast with management of multicast group membership
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services

Definitions

  • the embodiments of the present application relate to the field of computer technologies, and in particular, to a group type identification method, apparatus, computer device, and medium.
  • the embodiments of the present application provide a group type identification method, apparatus, computer equipment and medium, which improve the identification accuracy rate of the group type.
  • the technical solution is as follows:
  • a group type identification method comprising:
  • the first target graph is constructed according to the association relationship between the plurality of first user nodes;
  • an attention parameter of each first user node is obtained, where the attention parameter indicates the importance of the first user node in the first target graph degree;
  • the attention parameters of the plurality of second user nodes are greater than the attention parameters of the unselected first user nodes
  • the relationship between the two user nodes is constructed.
  • a group type identification device comprising:
  • a feature acquisition module configured to acquire first graph structural features of the first target graph and first user features of multiple first user nodes in the first target graph, where the first user nodes are users in the target group Identifying corresponding nodes, the first target graph is constructed according to the association relationship between the plurality of first user nodes;
  • a first attention acquisition module configured to acquire attention parameters of each first user node in the first target graph based on the first graph structural feature and a plurality of first user features, and the attention parameters represent the degree of importance of the first user node in the first target graph;
  • a first screening module configured to select a plurality of second user nodes from the plurality of first user nodes, the attention parameters of the plurality of second user nodes are greater than the attention parameters of the unselected first user nodes ;
  • a type identification module configured to identify the group type of the target group based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics of the second target graph, where the second target graph is It is constructed according to the association relationship between the plurality of second user nodes.
  • the apparatus further includes:
  • a feature adjustment module configured to adjust the first user features of the multiple second user nodes based on the second graph structure feature to obtain the second user features of the multiple second user nodes;
  • a second attention obtaining module configured to obtain attention parameters of each second user node in the second target graph based on the second graph structure feature and a plurality of second user features
  • the second screening module is configured to select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than the attention parameters of the unselected second user nodes .
  • the type identification module is configured to, based on the first user characteristics of the plurality of second user nodes, the second graph structure characteristics, and the first user characteristics of the plurality of third user nodes.
  • the second user feature and the third graph structure feature of the third target graph identify the group type of the target group, and the third target graph is constructed according to the association relationship between the plurality of third user nodes.
  • the type identification module includes:
  • a first fusion unit configured to fuse the first user features and the second graph structure features of the plurality of second user nodes to obtain a first fusion feature
  • a second fusion unit configured to fuse the second user features of the plurality of third user nodes and the third graph structure features to obtain second fusion features
  • a type identification unit configured to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the first fusion unit is configured to:
  • averaging is performed based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics to obtain the average user characteristics
  • the first fusion feature is obtained by splicing the average user feature and the largest user feature among the first user features of the plurality of second user nodes.
  • the type identification unit is used for:
  • a group type of the target group is identified.
  • the group type recognition model includes a first attention network, a first screening network and a recognition network,
  • the first attention acquisition module is configured to call the first attention network to acquire each first image in the first target image based on the first image structure feature and the plurality of first user features. Attention parameters of user nodes;
  • the first screening module configured to invoke the first screening network to select the plurality of second user nodes from the plurality of first user nodes;
  • the type identification module is configured to invoke the identification network to identify the group type of the target group based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics.
  • the group type identification model further includes a first convolutional network, a second attention network and a second screening network
  • the apparatus further includes:
  • a feature adjustment module configured to call the first convolutional network, adjust the first user features of the plurality of second user nodes based on the second graph structural features, and obtain the first user features of the plurality of second user nodes.
  • a second attention acquisition module configured to call the second attention network, and acquire the attention of each second user node in the second target graph based on the second graph structure feature and multiple second user features force parameter;
  • the second screening module is configured to call the second screening network, and select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than those that have not been selected.
  • the attention parameter of the second user node is configured to call the second screening network, and select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than those that have not been selected.
  • the attention parameter of the second user node is configured to call the second screening network, and select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than those that have not been selected.
  • the type identification module configured to invoke the identification network, is based on the first user characteristics of the plurality of second user nodes, the second graph structure characteristics, the plurality of second user nodes
  • the second user feature of the third user node and the third graph structure feature of the third target graph identify the group type of the target group, and the third target graph is based on the relationship between the plurality of third user nodes. relationship is built.
  • the group type identification model further includes a first fusion network and a second fusion network
  • the type identification module includes:
  • a first fusion unit configured to invoke the first fusion network to fuse the first user features of the plurality of second user nodes and the second graph structure features to obtain the first fusion features
  • a second fusion unit configured to invoke the second fusion network, and fuse the second user features of the plurality of third user nodes and the third graph structure features to obtain a second fusion feature
  • a type identification unit configured to invoke the identification network to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the group type identification model further includes a splicing network, and the type identification unit is configured to:
  • the identification network is invoked to identify the group type of the target group based on the splicing feature.
  • the training process of the group type identification model includes:
  • the group type recognition model is trained according to the difference between the sample type and the prediction type.
  • the first screening network is used for:
  • the graph structure feature includes an association degree between any two user nodes among the multiple user nodes, and the feature acquisition module is configured to:
  • co-occurrence times of any two user identities in the target group where the co-occurrence times refer to publishing content in the target group based on the any two user identities within multiple reference time periods the number of times;
  • the degree of association between any two user identifiers is determined, and the association degree feature is positively correlated with the co-occurrence times.
  • the user features include user behavior features and user attribute features
  • the feature acquisition module is configured to:
  • the user social network includes multiple registered user identities
  • the user social network obtain the user behavior characteristics of the multiple user identifiers
  • the user attribute features of the multiple user IDs are acquired.
  • a computer device in another aspect, includes a processor and a memory, the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the The operations performed in the group type identification method described in the above aspects.
  • a computer-readable storage medium is provided, and at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the above-mentioned aspects.
  • a computer program product or computer program comprising computer program code, the computer program code being stored in a computer-readable storage medium, the processor of the computer device from A computer-readable storage medium reads the computer program code, and the processor executes the computer program code, so that the computer device implements the operations performed in the group type identification method described in the above aspects.
  • the graph structure feature and the user node feature are taken into consideration. Compared with only acquiring user information in the prior art, the amount of information is increased, so that the obtained The attention parameter can more accurately reflect the importance of user nodes in the graph structure, so that when multiple first user nodes are screened according to the reference attention parameters, the more important user nodes can be accurately selected.
  • the user characteristics and graph structure characteristics of user nodes are used to identify the group type of the target group to improve the recognition accuracy, and at the same time, unimportant user nodes are discarded to reduce the amount of data processed and improve the processing speed.
  • FIG. 1 is a flowchart of a method for identifying a group type provided by an embodiment of the present application
  • FIG. 2 is a schematic structural diagram of a group type identification model provided by an embodiment of the present application.
  • FIG. 3 is a schematic structural diagram of another group type identification model provided by an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of another group type identification model provided by an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of another group type identification model provided by an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a group type identification device provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of another group type identification device provided by an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a terminal provided by an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • first and second used in this application may be used herein to describe various concepts, but these concepts are not limited by these terms unless otherwise specified. These terms are only used to distinguish one concept from another.
  • the first user node may be referred to as the second user node
  • the second user node may be referred to as the first user node.
  • the terms “at least one”, “plurality”, “each”, “any one”, etc. used in this application at least one includes one, two or more, multiple includes two or more, each includes Each refers to each of the corresponding plurality, and any refers to any one of the plurality.
  • the multiple user nodes include 3 user nodes, and each user node refers to each of the 3 user nodes, and any refers to any one of the 3 user nodes, and can be the third user node. One, it could be the second, it could be the third.
  • the type of the group is determined according to user information corresponding to multiple user identifiers in the group.
  • the user information includes user avatar, user label, age, gender and other information.
  • this method of determining the group type based on the user information is based on a small amount of data, resulting in a low accuracy rate of the determined group type.
  • the group type identification method provided by the embodiment of the present application is applied in a group type identification scenario.
  • the group type identification method provided by the embodiment of the present application is used to identify whether the target group is a specific group, and if it is determined that the target group is a specific group group, the target group will be blocked or otherwise processed to avoid losses to users.
  • the methods provided in the embodiments of the present application are executed by a computer device, where the computer device is a terminal or a server.
  • the terminal is a portable, pocket-sized, hand-held and other various types of terminals, such as a mobile phone, a computer, a tablet computer, and the like.
  • a server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, Cloud servers for basic cloud computing services such as domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.
  • CDN Content Delivery Network
  • FIG. 1 is a flowchart of a group type identification method provided by an embodiment of the present application.
  • the execution body of the embodiments of the present application is a computer device. Referring to Figure 1, the method includes the following steps:
  • a target group including multiple user identifiers is used as an example for description.
  • the first user node is a node corresponding to the user identifier in the target group, and the first target graph is constructed according to the association relationship between a plurality of first user nodes.
  • the first target graph is the initial target graph; in the case where multiple first user nodes are user nodes corresponding to some user identities in the target group, that is, multiple first user nodes have been obtained after screening, then the first target graph is screened The resulting target image.
  • the first graph structural feature is used to describe the first target graph, and the first target graph includes a plurality of first user nodes and connecting lines between the plurality of first user nodes. The connecting line between them indicates that there is an association relationship between any two first user nodes.
  • the first user feature is used to describe the user corresponding to the user identification, and the first user feature includes at least one of a user behavior feature or a user attribute feature.
  • the attention parameter represents the importance degree of the first user node in the first target graph, that is, represents the importance degree of the user ID corresponding to the first user node in the target group.
  • the importance of user identity in the target group is positively correlated with the attention parameter. For example, users such as group owners and administrators in the target group are more important in the target group, and the attention parameters of the user nodes corresponding to these users will also be larger.
  • a plurality of first user nodes are screened, and a plurality of second user nodes with larger attention parameters are selected from the plurality of first user nodes, and then according to the second user nodes with larger attention parameters Nodes are used for processing, thereby discarding some unimportant information in the processing process, ensuring that while reducing the amount of data, the loss of important information is reduced.
  • the second target graph is constructed according to the association relationship between multiple second user nodes, and the second graph structural feature is used to describe the second target graph.
  • the second target graph is a subgraph of the first target graph.
  • the second target graph includes a plurality of second user nodes and connecting lines between the plurality of second user nodes.
  • the second target graph includes a plurality of second user nodes.
  • the connection lines between the nodes are the same as the connection lines of the plurality of second user nodes in the first target graph.
  • the group type refers to the type to which the target group belongs, and different group types can be divided according to different division standards. For example, according to the occupation of the user corresponding to the user ID in the target group, a work group and a non-work group are divided; according to whether the users corresponding to the user ID in the target group are relatives, a family group and a non-work group are divided. Family group; according to whether the behavior of the user corresponding to the user ID in the target group in the target group belongs to a specific behavior, a specific group and a non-specific group are divided. For example, certain conduct includes conduct involving pornography, gambling, fraud, etc.
  • the method provided by the embodiment of the present application considers the graph structure feature and the user node feature to obtain the attention parameter of each first user node. Compared with only obtaining user information in the prior art, the amount of information is increased, so that the obtained The attention parameter can more accurately reflect the importance of user nodes in the graph structure, so that when multiple first user nodes are screened according to the reference attention parameters, the more important user nodes can be accurately selected.
  • the user characteristics and graph structure characteristics of user nodes are used to identify target groups to improve the recognition accuracy. At the same time, discarding unimportant user nodes can reduce the amount of data processed and improve the processing speed.
  • the computer device invokes the group type identification model to identify the group type of the target group.
  • the model structure of the group type identification model is described below.
  • FIG. 2 is a schematic structural diagram of a group type identification model provided by an embodiment of the present application.
  • the group type recognition model includes an input network 201 , a first attention network 202 , a first screening network 203 and a recognition network 204 .
  • the input network 201 is connected to the first attention network 202
  • the first attention network 202 is connected to the first screening network 203
  • the first screening network 203 is connected to the recognition network 204 .
  • the input network 201 is used to obtain the input graph structure features and user features
  • the first attention network 202 is used to obtain the attention parameters of each user node
  • the first screening network 203 is used to filter according to the obtained attention parameters.
  • important user nodes are selected
  • the identification network 204 is configured to identify the group type according to the user characteristics of the filtered user nodes and the corresponding graph structure characteristics.
  • the group type identification model further includes a first convolutional network 205 , a second attention network 206 , a second screening network 207 and a splicing network 208 .
  • the first convolutional network 205 is connected with the first screening network 203 and the second attention network 206
  • the second attention network 206 is connected with the second screening network 207
  • the second screening network 207 is connected with the recognition network 204
  • the splicing network 208 is connected to the first screening network 203, the second screening network 207 and the identification network 204.
  • the first convolutional network 205 is used to further process the user characteristics of the more important user nodes screened by the first screening network 203
  • the second attention network 206 is used to obtain the attention of each screened user node parameter
  • the second screening network 207 is used to further screen the screened user nodes according to the acquired attention parameters
  • the splicing network 208 is used to splicing the user characteristics of the user nodes screened by the first screening network 203 and the second screening network.
  • 207 User characteristics of the user nodes that are filtered out again.
  • the user node is screened twice as an example for illustration.
  • the user node can be screened three times, four times or even more times to obtain more information. quantity.
  • three attention networks and three screening networks are used to screen user nodes three times.
  • the splicing network is used to splicing the user characteristics of the user nodes screened by the three screening networks.
  • a convolution network is also included before the first attention network, that is, the input user features and graph structure features are first subjected to convolution processing, and the first attention network User features and graph structure features to obtain attention parameters.
  • the following describes an example of invoking a group type identification model to identify the type of a target group by using the embodiment shown in FIG. 5 .
  • FIG. 5 is a flowchart of a group type identification method provided by an embodiment of the present application.
  • the execution body of the embodiments of the present application is a computer device. Referring to Figure 5, the method includes the following steps:
  • the embodiments of the present application are described by taking the first target graph as an initially constructed target graph as an example.
  • corresponding user nodes are constructed according to multiple user identifiers in the target group, and then multiple user nodes are connected together according to the association relationship between multiple user identifiers to form a first target graph.
  • the first target graph is an isomorphic graph, that is, the first target graph includes only one type of node, the user node, and the user nodes are connected according to the same type of association relationship, that is, the connection in the first target graph line is the same type of connection line.
  • association relationship between any two user nodes is determined according to the association degree feature between the any two user nodes, and the association degree feature represents the degree of intimacy between the users corresponding to the any two user nodes.
  • the co-occurrence times of any two user identities in the target group are obtained, and the correlation feature between any two user identities is determined according to the co-occurrence times.
  • the correlation feature can be called the correlation degree
  • the number of co-occurrences refers to the number of times that any two user IDs jointly publish content in the target group within multiple reference time periods, and the correlation degree is positively correlated with the number of co-occurrences. , that is, the greater the number of co-occurrences, the greater the degree of association between the two user identities, and the smaller the number of co-occurrences, the smaller the degree of association between the two user identities.
  • the number of co-occurrences is directly used as the degree of association.
  • the reference time period is 5 minutes.
  • the number of co-occurrences is incremented by 1, and in the case that only one user speaks, or when neither user speaks, the number of co-occurrences does not increase.
  • the degree of association can be determined according to the number of co-occurrences.
  • the number of co-occurrences is set to 0, thus avoiding chance cases. For example, if a group owner in a target group publishes a message, all users in the target group except the group owner may reply to the message. At this time, many users in the target group will speak at the same time, but these may not have a close relationship between users.
  • the first target graph is an homogeneous graph for description.
  • the first target graph is a heterogeneous graph.
  • the first target graph includes the user A node and a user type node, the user nodes are connected according to the same type of association relationship, and the user node and the user type node are connected according to the type of the user corresponding to the user node.
  • the first graph structural feature represents a plurality of user nodes in the first target graph and the association relationship between the plurality of user nodes, and the first graph structural feature includes a relationship between any two user nodes in the plurality of user nodes. Correlation.
  • the first user feature includes a user behavior feature and a user attribute feature, the user behavior feature represents the user's historical behavior, and the user attribute feature represents the user's own attribute.
  • a user social network is obtained, and according to the user social network, user behavior characteristics identified by multiple users in the target group are obtained.
  • the user social network includes multiple registered user identifiers.
  • the user behavior characteristics identified by the user are acquired according to the user's social network in a graph embedding manner.
  • the graph embedding is used to represent each node in the graph as a dense vector in a low-dimensional space, and the obtained dense vector is used as the feature information of the node.
  • the core idea of the graph embedding is to preserve the intrinsic structural properties of the graph structure, that is, Keep nodes connected in a graph close to each other in a vector space.
  • graph embedding methods include DeepWalk (a method for generating node representations in a network) and Node2Vec (a model for generating node vectors in a network) and other node embedding methods.
  • Deepwalk node embedding method As an example, according to the user's social network, starting from each user node in the graph, according to the user's social network and the user's connection weight, randomly walk multiple trajectories, and use all the trajectories as the corpus input. To the word2vec word vector embedding model, the word2vec word vector embedding model is used for processing, and finally the user behavior characteristics of each user node are obtained.
  • user attribute features of multiple user IDs in the target group are acquired according to user portrait information corresponding to multiple user IDs in the target group.
  • the user portrait information includes information such as user portrait, dynamic information published by the user, user age, gender, and geographic location where the user is located.
  • a vector is used to represent the first user characteristics
  • a matrix is used to represent multiple first user characteristics, that is, a matrix is formed by splicing the multiple first user characteristics together. For example, a certain row or a certain column in the matrix represents the first user characteristic of a first user node.
  • a matrix is used to represent the structural feature of the first graph, and an element of each position in the matrix represents an association relationship between corresponding two user nodes.
  • the elements in the third row and the fourth column in the matrix represent the association relationship between the third user node and the fourth user node.
  • the following formula is used to obtain the association relationship between any two first user nodes:
  • a ij log(C ij );
  • a ij represents the association relationship between the i-th first user node and the j-th user node
  • C ij represents the common relationship between the user ID corresponding to the i-th first user node and the user ID corresponding to the j-th user node The number of occurrences.
  • the first attention network is at least one GNN (Graph Neural Network, graph neural network). Use any of the following formulas to obtain the attention parameter:
  • Z represents the attention parameter
  • X represents multiple first user features
  • A represents the first graph structure feature
  • ⁇ is the reference value
  • GNN( ) represents the convolution processing of the first user feature and the first graph structure feature
  • m denotes the mth GNN
  • M denotes the number of GNNs.
  • One GNN is used in the first and second formula above, two GNNs are used in the third formula above, and M GNNs are used in the fourth formula above.
  • the two GNNs are connected in sequence, that is, the first GNN is used for one processing, and then the second GNN is used for the second processing based on the first processing to obtain the attention parameters.
  • the M GNNs process the first user feature and the first graph structure feature respectively, and average the M attention parameters obtained by processing to obtain the final attention parameter.
  • Invoke the first screening network select a plurality of second user nodes from the plurality of first user nodes, and construct a second target graph according to the association relationship among the plurality of second user nodes.
  • the attention parameters of the plurality of second user nodes are greater than the attention parameters of the unselected first user nodes.
  • the first screening network is called, the number of multiple first user nodes is multiplied by the reference ratio to obtain the reference number, and the attention parameters of the multiple first user nodes are sorted in descending order. Arrange in order, select the reference number of attention parameters arranged in the front, and use the first user node corresponding to the selected attention parameter as the second user node. After the plurality of second user nodes are selected, a second target graph is formed according to the association relationship between the selected plurality of second user nodes.
  • the following formula is used to obtain the first user features of multiple second user nodes and the second graph structure features of the second target graph:
  • X′ represents the first user features of multiple second user nodes
  • X idx represents the first user feature of the second user node selected from the first user features of multiple first user nodes
  • a l represents the first user feature of the second user node.
  • the second graph structural feature, A idx, idx represents the association relationship corresponding to a plurality of second user nodes selected from the first graph structural feature.
  • the following formula is used to adjust the first user characteristics of multiple second user nodes to obtain the adjusted first user characteristics:
  • X l represents the adjusted first user feature
  • Z represents the attention parameters of multiple second user nodes
  • ⁇ represents the bitwise product, that is, the first user feature of each second user node is multiplied by the attention parameter .
  • the embodiment of the present application is only described by taking multiple screening of user nodes as an example.
  • a plurality of second After the user node, a plurality of second After the user node, the group type of the target group is identified directly based on the user features of the plurality of second user nodes and the second graph structure feature for constructing the second target graph, and subsequent steps are not performed.
  • the first convolutional network is GCN (Graph Convolutional Networks, graph convolutional network), and the following formula is used to determine the second user feature after convolution processing:
  • X l+1 represents the second user features of multiple second user nodes
  • X l represents the first user features of multiple second user nodes
  • a l represents the second graph structure feature
  • W l+1 represents the first user feature of the multiple second user nodes.
  • the model parameters in the convolutional network, ⁇ is the reference value.
  • Invoke the second screening network select a plurality of third user nodes from the plurality of second user nodes, and construct a third target graph according to the association relationship among the plurality of third user nodes.
  • steps 506 to 507 are the same as the implementations of the above-mentioned steps 503 to 504, and are not repeated here.
  • first user features and second graph structure features of multiple second user nodes are first fused to obtain first fusion features; multiple third users are fused.
  • the second user feature and the third graph structure feature of the node are used to obtain the second fusion feature; the identification network is invoked to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • an average processing is performed on the first user characteristics and the second graph structure characteristics of the plurality of second user nodes, that is, based on the first user characteristics of the plurality of second user nodes
  • the average user feature and the second graph structure feature are averaged to obtain the average user feature corresponding to the multiple second user nodes; the average user feature and the largest user feature among the first user features of the multiple second user nodes are spliced to obtain the first fusion feature.
  • the second graph structure feature includes the association relationship between multiple second user nodes, for any second user node, the second user node and other second user nodes can be determined from the second graph structure feature.
  • the relationship between user nodes, when calculating the mean value, the first user feature of the second user node and the relationship between the second user node and other second user nodes are taken as a whole.
  • the first user characteristics of the plurality of second user nodes and the plurality of association relationships are averaged.
  • s represents the first fusion feature
  • N represents the number of second user nodes
  • x i represents the first user feature of the ith second user node and the relationship between the ith second user node and other second user nodes.
  • represents the splicing of the features before
  • the average processing is performed on the second user feature and the third graph structure feature of multiple third user nodes. , that is, based on the average of the second user features and the third graph structure features of multiple third user nodes, the average user features corresponding to multiple third user nodes are obtained; The largest user feature among the user features is obtained to obtain the second fusion feature.
  • the first fusion feature and the second fusion feature are spliced to obtain a splicing feature corresponding to the target group, and based on the splicing feature, a group type corresponding to the target group is identified.
  • the splicing feature is the feature representing the target group.
  • the group type identification model further includes a first fusion network, a second fusion network, and a splicing network, that is, the first fusion network is called, and a plurality of second fusion networks are called.
  • the first user feature and the second graph structure feature of the user node are used to obtain the first fusion feature;
  • the second fusion network is called to fuse the second user feature and the third graph structure feature of multiple third user nodes to obtain the second fusion feature. ;
  • the recognition network is a classifier
  • the classifier includes a multi-layer perceptron
  • the output of the recognition network is 0 or 1.
  • the output of the network is 0, it indicates that the target group is not a specific group, and when the output of the recognition network is 1, it indicates that the target group is a specific group.
  • E represents the splicing feature
  • MLP( ⁇ ) means that MLP (Multi-Layer Perceptron) is used to process the splicing feature.
  • the output of the identification network is a probability.
  • the output probability is greater than the reference probability, it indicates that the target group is a specific group, and when the output probability is not greater than the reference probability, it indicates the target group.
  • Groups are not specific groups.
  • the computer device can directly Structural features and multiple first user features, obtain the attention parameters of each first user node, select multiple second user nodes from multiple first user nodes, and according to the association relationship between multiple second user nodes Construct a second target graph, adjust the first user features of multiple second user nodes according to the second graph structural features of the second target graph, and obtain the second user features of multiple second user nodes, based on the second graph structural features and multiple second user features, obtain the attention parameters of each second user node, select multiple third user nodes from multiple second user nodes, and construct the first user node according to the association relationship between multiple third user nodes.
  • Three target graphs identifying target groups based on first user features of multiple second user nodes, second graph structural features, second user features of multiple third user nodes, and third graph structural features of the third target graph group type.
  • the graph structure feature and the user node feature are taken into consideration. Compared with only acquiring user information in the prior art, the amount of information is increased, so that the obtained The attention parameter can more accurately reflect the importance of user nodes in the graph structure, so that when multiple first user nodes are screened according to the reference attention parameters, the more important user nodes can be accurately selected.
  • the user characteristics and graph structure characteristics of user nodes are used to identify the group type of the target group to improve the recognition accuracy, and at the same time, unimportant user nodes are discarded to reduce the amount of data processed and improve the processing speed.
  • the user nodes are screened multiple times to obtain user features and graph structure features at different levels.
  • the user features and graph structure features at different levels are considered, which further improves the accuracy of identification. Rate.
  • the group type identification model needs to be trained first.
  • the training process of the group type identification model includes: obtaining the sample type of the sample group and the sample graph structure of the sample graph Features and sample user characteristics of multiple sample user nodes in the sample target graph; call the group type identification model to identify the prediction type of the sample group based on the sample graph structural characteristics and the sample user characteristics of multiple sample user nodes; based on the sample type and predict the difference between the types, training the cohort type recognition model.
  • the sample user node is a node corresponding to the sample user ID, and the sample graph is constructed according to the association relationship between multiple sample user IDs in the sample group.
  • the target group can be used to continue training the group type identification model.
  • the above training process is only an example of one training, and in another embodiment, the group type identification model can be iteratively trained for multiple times.
  • a keyword filtering technology is used to determine whether the content published by the user includes a specific word, and if it includes a specific word, then It is considered that the target group belongs to a specific group, but if the user uses other non-specific vocabulary to replace the corresponding specific vocabulary, the keyword filtering technology cannot be used to detect it, and it is impossible to determine whether it is a specific vocabulary. Accuracy is low.
  • the method of user reporting is adopted, that is, after the user reports the target group, the technician will manually review it to determine whether the target group belongs to a specific group, but this method depends on the user Affected by the user's reporting behavior, the identification accuracy of the target group is also low.
  • the method provided by the embodiment of the present application is not affected by the vocabulary used by the user and the reporting behavior of the user, and can directly The user characteristics of the node identify the group type of the target group, which improves the identification accuracy.
  • the pooling function is used to read the information of all user nodes in the target group, but when there are many user nodes, it is difficult to read all the information by using the pooling function, which will lead to loss of a lot of information , and the more user nodes, the slower the processing speed.
  • user nodes can be screened according to the attention parameter, and only the information of the screened user nodes can be read out, and the information of important nodes can be guaranteed to be retained according to the size of the attention parameter, which will not cause A large amount of information is lost, and the processing speed is improved while ensuring the amount of information.
  • FIG. 7 is a schematic structural diagram of a group type identification device provided by an embodiment of the present application. Referring to Figure 7, the device includes:
  • the feature acquisition module 701 is used to acquire the first graph structure feature of the first target graph and the first user features of a plurality of first user nodes in the first target graph, where the first user nodes are corresponding to the user IDs in the target group. node, and the first target graph is constructed according to the association relationship between a plurality of first user nodes;
  • the first attention obtaining module 702 is configured to obtain the attention parameter of each first user node in the first target graph based on the first graph structure feature and the plurality of first user features, where the attention parameter indicates that the first user node is in The degree of importance in the first objective graph;
  • the first screening module 703 is configured to select a plurality of second user nodes from a plurality of first user nodes according to the obtained plurality of attention parameters, and the attention parameters of the plurality of second user nodes are greater than the unselected first user nodes.
  • the type identification module 704 is used to identify the group type of the target group based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics of the second target graph, and the second target graph is based on the plurality of second target graphs.
  • the association relationship between user nodes is constructed.
  • the graph structure feature and the user node feature are considered. Compared with only acquiring user information in the prior art, the amount of information is increased, so that the obtained The attention parameter can more accurately reflect the importance of user nodes in the graph structure, so that when multiple first user nodes are screened according to the reference attention parameters, the more important user nodes can be accurately selected.
  • the information of user nodes is used to identify the group type of the target group to improve the recognition accuracy, and at the same time, unimportant user nodes are discarded to reduce the amount of data processed and improve the processing speed.
  • the apparatus further includes:
  • a feature adjustment module 705, configured to adjust the first user features of a plurality of second user nodes based on the structural features of the second graph to obtain second user features of a plurality of second user nodes;
  • the second attention acquisition module 706 is configured to process based on the second graph structure feature and a plurality of second user features, and obtain the attention parameter of each second user node;
  • the second screening module 707 is configured to select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than the attention parameters of the unselected second user nodes.
  • the type identification module 704 is configured to, based on the first user characteristics of the plurality of second user nodes, the second graph structure characteristics, the second user characteristics of the plurality of third user nodes, and the third target
  • the third graph structural feature of the graph identifies the group type of the target group, and the third target graph is constructed according to the association relationship between a plurality of third user nodes.
  • the type identification module 704 includes:
  • a first fusion unit 7041 configured to fuse first user features and second graph structure features of multiple second user nodes to obtain first fusion features
  • a second fusion unit 7042 configured to fuse the second user features and the third graph structure features of multiple third user nodes to obtain second fusion features
  • the type identification unit 7043 is configured to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the first fusion unit 7041 is used for:
  • a first fusion feature is obtained by splicing the average user feature and the largest user feature among the first user features of the plurality of second user nodes.
  • the type identification unit 7043 is used for:
  • the group type of the target group is identified.
  • the group type recognition model includes a first attention network, a first screening network and a recognition network,
  • the first attention obtaining module 702 is used to call the first attention network, and obtain the attention parameters of each first user node based on the first graph structure feature and the plurality of first user features;
  • a first screening module 703, configured to invoke a first screening network to select a plurality of second user nodes from a plurality of first user nodes;
  • the type identification module 704 is configured to invoke the identification network to identify the group type of the target group based on the first user characteristics and the second graph structure characteristics of the plurality of second user nodes.
  • the group type identification model further includes a first convolutional network, a second attention network, and a second screening network.
  • the apparatus further includes:
  • the feature adjustment module 705 is configured to call the first convolutional network, and based on the second graph structure feature, adjust the first user features of the plurality of second user nodes to obtain the second user features of the plurality of second user nodes;
  • the second attention obtaining module 706 is configured to call the second attention network, and obtain the attention parameters of each second user node in the second target graph based on the second graph structure feature and the plurality of second user features;
  • the second screening module 707 is configured to invoke the second screening network, and select a plurality of third user nodes from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than those of the unselected second user nodes. attention parameter.
  • the type identification module 704 is configured to invoke the identification network, based on the first user characteristics of the plurality of second user nodes, the second graph structure characteristics, and the second users of the plurality of third user nodes.
  • the feature and the third graph structure feature of the third target graph identify the group type of the target group, and the third target graph is constructed according to the association relationship between a plurality of third user nodes.
  • the group type identification model further includes a first fusion network and a second fusion network.
  • the type identification module 704 includes:
  • the first fusion unit 7041 is configured to call the first fusion network, and fuse the first user features and the second graph structure features of multiple second user nodes to obtain the first fusion features;
  • the second fusion unit 7042 is configured to call the second fusion network, and fuse the second user features and the third graph structure features of a plurality of third user nodes to obtain the second fusion features;
  • the type identification unit 7043 is configured to invoke the identification network to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the group type identification model further includes a splicing network.
  • the type identification unit 7043 is used for:
  • the recognition network is called to identify the group type of the target group based on the splicing features.
  • the training process of the group type recognition model includes:
  • the sample user node is the node corresponding to the sample user ID, and the sample graph is based on the The association relationship between multiple sample user IDs is constructed;
  • the cohort type recognition model is trained based on the difference between sample type and prediction type.
  • the first screening network 703 is used for:
  • the graph structure feature includes a correlation feature between any two user nodes among the multiple user nodes.
  • the feature acquisition module 701 is configured to:
  • co-occurrence times of any two user identities in the target group and the co-occurrence times refer to the number of times that content is jointly published in the target group based on any two user identities within multiple reference time periods;
  • the degree of association between any two user IDs is determined, and the degree of association is positively correlated with the number of co-occurrences.
  • the user features include user behavior features and user attribute features.
  • the feature acquisition module 701 is used for:
  • the user's social network includes multiple registered user IDs
  • the user attribute features of the multiple user IDs are acquired.
  • the group type identification device when the group type identification device provided in the above embodiment identifies the group type, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be assigned to different functions Module completion, that is, dividing the internal structure of the computer device into different functional modules to complete all or part of the functions described above.
  • the group type identification device and the group type identification method embodiments provided by the above embodiments belong to the same concept, and the specific implementation process thereof is detailed in the method embodiments, which will not be repeated here.
  • the embodiment of the present application also provides a computer device, the computer device includes a processor and a memory, and at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the first target graph is constructed according to the association relationship between the plurality of first user nodes;
  • an attention parameter of each first user node is obtained, where the attention parameter indicates the importance of the first user node in the first target graph degree;
  • the attention parameters of the plurality of second user nodes are greater than the attention parameters of the unselected first user nodes
  • the relationship between the two user nodes is constructed.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a plurality of third user nodes are selected from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than the attention parameters of the unselected second user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the second graph structure feature, the second user feature of the plurality of third user nodes, and the third graph structure feature of the third target graph identify the the group type of the target group, and the third target graph is constructed according to the association relationship between the plurality of third user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a group type of the target group is identified.
  • the at least one computer program is loaded and executed by a processor to implement the following steps: averaging based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics, get the average user characteristics;
  • the first fusion feature is obtained by splicing the average user feature and the largest user feature among the first user features of the plurality of second user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a group type of the target group is identified.
  • the group type recognition model includes a first attention network, a first screening network and a recognition network, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the selecting multiple second user nodes from the multiple first user nodes includes:
  • the identification network is invoked to identify the group type of the target group based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics.
  • the group type identification model further includes a first convolution network, a second attention network and a second screening network, and the at least one computer program is loaded and executed by the processor to implement the following steps :
  • the attention parameters of the plurality of third user nodes are greater than the attention of the unselected second user nodes parameter.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the graph structure feature identifies the group type of the target group, and the third target graph is constructed according to the association relationship between the plurality of third user nodes.
  • the group type identification model further includes a first fusion network and a second fusion network
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the identification network is invoked to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the group type identification model further includes a splicing network, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the identification network is invoked to identify the group type of the target group based on the splicing feature.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the group type recognition model is trained according to the difference between the sample type and the prediction type.
  • the graph structure feature includes the degree of association between any two user nodes in the plurality of user nodes, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • co-occurrence times of any two user identities in the target group where the co-occurrence times refer to publishing content in the target group based on the any two user identities within multiple reference time periods the number of times;
  • the degree of association between any two user identifiers is determined, and the association degree is positively correlated with the co-occurrence times.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the user characteristics include user behavior characteristics and user attribute characteristics
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the user social network includes multiple registered user identities
  • the user social network obtain the user behavior characteristics of the multiple user identifiers
  • the user attribute features of the multiple user IDs are acquired.
  • FIG. 9 is a schematic structural diagram of a terminal 900 provided by an embodiment of the present application.
  • the terminal 900 includes: a processor 901 and a memory 902 .
  • the processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like.
  • the processor 901 may also include a main processor and a co-processor.
  • the main processor is a processor used to process data in the wake-up state, also called CPU (Central Processing Unit, central processing unit); the co-processor is A low-power processor for processing data in a standby state.
  • the processor 901 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is used for rendering and drawing the content that needs to be displayed on the display screen.
  • GPU Graphics Processing Unit, image processor
  • Memory 902 may include one or more computer-readable storage media, which may be non-transitory.
  • the non-transitory computer-readable storage medium in the memory 902 is used to store at least one computer program, and the at least one computer program is used to be executed by the processor 901 to implement the methods provided by the method embodiments in this application. Group type identification method.
  • the terminal 900 may optionally further include: a peripheral device interface 903 and at least one peripheral device.
  • the processor 901, the memory 902 and the peripheral device interface 903 may be connected through a bus or a signal line.
  • Each peripheral device can be connected to the peripheral device interface 903 through a bus, a signal line or a circuit board.
  • the peripheral device includes: at least one of a radio frequency circuit 904 , a display screen 905 , a camera assembly 906 , an audio circuit 907 and a power supply 908 .
  • the peripheral device interface 903 may be used to connect at least one peripheral device related to I/O (Input/Output) to the processor 901 and the memory 902 .
  • processor 901, memory 902, and peripherals interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one of processor 901, memory 902, and peripherals interface 903 or The two can be implemented on a separate chip or circuit board, which is not limited in this embodiment.
  • the radio frequency circuit 904 is used for receiving and transmitting RF (Radio Frequency, radio frequency) signals, also called electromagnetic signals.
  • the radio frequency circuit 904 communicates with the communication network and other communication devices through electromagnetic signals.
  • the radio frequency circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals.
  • the display screen 905 is used for displaying UI (User Interface, user interface).
  • the UI can include graphics, text, icons, video, and any combination thereof.
  • the display screen 905 also has the ability to acquire touch signals on or above the surface of the display screen 905 .
  • the touch signal may be input to the processor 901 as a control signal for processing.
  • the camera assembly 906 is used to capture images or video.
  • the camera assembly 906 includes a front camera and a rear camera.
  • Audio circuitry 907 may include a microphone and speakers.
  • the microphone is used to collect the sound waves of the user and the environment, convert the sound waves into electrical signals, and input them to the processor 901 for processing, or to the radio frequency circuit 904 to realize voice communication.
  • Power supply 908 is used to power various components in terminal 900 .
  • the power source 908 may be alternating current, direct current, primary batteries, or rechargeable batteries.
  • FIG. 9 does not constitute a limitation on the terminal 900, and may include more or less components than shown, or combine some components, or adopt different component arrangements.
  • the computer device is provided as a server.
  • 10 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • the server 1000 may vary greatly due to different configurations or performance, and may include one or more processors (Central Processing Units, CPU) 1001 and a Or more than one memory 1002, wherein, at least one computer program is stored in the memory 1002, and the at least one computer program is loaded and executed by the processor 1001 to implement the methods provided by the above method embodiments.
  • the server may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface for input and output, and the server may also include other components for implementing device functions, which will not be described here.
  • Embodiments of the present application further provide a computer-readable storage medium, where at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the following steps:
  • the first target graph is constructed according to the association relationship between the plurality of first user nodes;
  • an attention parameter of each first user node is obtained, where the attention parameter indicates the importance of the first user node in the first target graph degree;
  • the attention parameters of the plurality of second user nodes are greater than the attention parameters of the unselected first user nodes
  • the relationship between the two user nodes is constructed.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a plurality of third user nodes are selected from the plurality of second user nodes, and the attention parameters of the plurality of third user nodes are greater than the attention parameters of the unselected second user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the second graph structure feature, the second user feature of the plurality of third user nodes, and the third graph structure feature of the third target graph identify the the group type of the target group, and the third target graph is constructed according to the association relationship between the plurality of third user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a group type of the target group is identified.
  • the at least one computer program is loaded and executed by a processor to implement the following steps: averaging based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics, get the average user characteristics;
  • the first fusion feature is obtained by splicing the average user feature and the largest user feature among the first user features of the plurality of second user nodes.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • a group type of the target group is identified.
  • the group type recognition model includes a first attention network, a first screening network and a recognition network, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the selecting multiple second user nodes from the multiple first user nodes includes:
  • the identification network is invoked to identify the group type of the target group based on the first user characteristics of the plurality of second user nodes and the second graph structure characteristics.
  • the group type identification model further includes a first convolution network, a second attention network and a second screening network, and the at least one computer program is loaded and executed by the processor to implement the following steps :
  • the attention parameters of the plurality of third user nodes are greater than the attention of the unselected second user nodes parameter.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the graph structure feature identifies the group type of the target group, and the third target graph is constructed according to the association relationship between the plurality of third user nodes.
  • the group type identification model further includes a first fusion network and a second fusion network
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the identification network is invoked to identify the group type of the target group based on the first fusion feature and the second fusion feature.
  • the group type identification model further includes a splicing network, and the at least one computer program is loaded and executed by the processor to realize the following steps:
  • the identification network is invoked to identify the group type of the target group based on the splicing feature.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the group type recognition model is trained according to the difference between the sample type and the prediction type.
  • the graph structure feature includes the degree of association between any two user nodes in the plurality of user nodes, and the at least one computer program is loaded and executed by the processor to implement the following steps:
  • co-occurrence times of any two user identities in the target group where the co-occurrence times refer to publishing content in the target group based on the any two user identities within multiple reference time periods the number of times;
  • the degree of association between any two user identifiers is determined, and the association degree is positively correlated with the co-occurrence times.
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the user characteristics include user behavior characteristics and user attribute characteristics
  • the at least one computer program is loaded and executed by the processor to implement the following steps:
  • the user social network includes multiple registered user identities
  • the user social network obtain the user behavior characteristics of the multiple user identifiers
  • the user attribute features of the multiple user IDs are acquired.
  • Embodiments of the present application also provide a computer program product or computer program, where the computer program product or computer program includes computer program code, and the computer program code is stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code, so that the computer device implements the operations performed in the group type identification method of the foregoing embodiment.

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Abstract

一种群组类型识别方法、装置、计算机设备及介质，属于计算机技术领域。获取第一目标图的第一图结构特征和第一目标图中多个第一用户节点的第一用户特征(101)；基于第一图结构特征和多个第一用户特征，获取第一目标图中每个第一用户节点的注意力参数(102)；从多个第一用户节点中选取多个第二用户节点，多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数(103)；基于多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别目标群组的群组类型(104)，根据筛选出的重要的用户节点的用户特征和图结构特征对目标群组进行识别，提高了识别准确率。

Description

群组类型识别方法、装置、计算机设备及介质
本申请要求于2021年01月04日提交、申请号为202110002127.7、发明名称为“群组类型识别方法、装置、计算机设备及介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及计算机技术领域，特别涉及一种群组类型识别方法、装置、计算机设备及介质。
背景技术
随着计算机技术和即时通信应用的发展，能够基于即时通信应用建立多种类型的群组，如何识别群组类型，已成为亟待解决的技术问题。
发明内容
本申请实施例提供了一种群组类型识别方法、装置、计算机设备及介质，提高了群组类型的识别准确率。所述技术方案如下：
一方面，提供了一种群组类型识别方法，所述方法包括：
获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
基于所述第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
另一方面，提供了一种群组类型识别装置，所述装置包括：
特征获取模块，用于获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
第一注意力获取模块，用于基于所述第一图结构特征和多个第一用户特征，获取所述第一目标图中每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
第一筛选模块，用于从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
类型识别模块，用于基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
在一种可能实现方式中，所述装置还包括：
特征调整模块，用于基于所述第二图结构特征，调整所述多个第二用户节点的第一用户 特征，得到所述多个第二用户节点的第二用户特征；
第二注意力获取模块，用于基于所述第二图结构特征和多个第二用户特征，获取所述第二目标图中每个第二用户节点的注意力参数；
第二筛选模块，用于从所述多个第二用户节点中选取多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在另一种可能实现方式中，所述类型识别模块，用于基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在另一种可能实现方式中，所述类型识别模块，包括：
第一融合单元，用于融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
第二融合单元，用于融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
类型识别单元，用于基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在另一种可能实现方式中，所述第一融合单元，用于：
根据所述第二用户节点的数量，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征进行求均值，得到平均用户特征；
拼接所述平均用户特征与所述多个第二用户节点的第一用户特征中的最大用户特征，得到所述第一融合特征。
在另一种可能实现方式中，所述类型识别单元，用于：
拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
基于所述拼接特征，识别所述目标群组的群组类型。
在另一种可能实现方式中，群组类型识别模型包括第一注意力网络、第一筛选网络和识别网络，
所述第一注意力获取模块，用于调用所述第一注意力网络，基于所述第一图结构特征和所述多个第一用户特征，获取所述第一目标图中每个第一用户节点的注意力参数；
所述第一筛选模块，用于调用所述第一筛选网络，从所述多个第一用户节点中选取所述多个第二用户节点；
所述类型识别模块，用于调用所述识别网络，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征，识别所述目标群组的群组类型。
在另一种可能实现方式中，所述群组类型识别模型还包括第一卷积网络、第二注意力网络和第二筛选网络，所述装置还包括：
特征调整模块，用于调用所述第一卷积网络，基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
第二注意力获取模块，用于调用所述第二注意力网络，基于所述第二图结构特征和多个第二用户特征，获取所述第二目标图中每个第二用户节点的注意力参数；
第二筛选模块，用于调用所述第二筛选网络，从所述多个第二用户节点中选取多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在另一种可能实现方式中，所述类型识别模块，用于调用所述识别网络，基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在另一种可能实现方式中，所述群组类型识别模型还包括第一融合网络和第二融合网络， 所述类型识别模块，包括：
第一融合单元，用于调用所述第一融合网络，融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
第二融合单元，用于调用所述第二融合网络，融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
类型识别单元，用于调用所述识别网络，基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在另一种可能实现方式中，所述群组类型识别模型还包括拼接网络，所述类型识别单元，用于：
调用所述拼接网络，拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
调用所述识别网络，基于所述拼接特征，识别所述目标群组的群组类型。
在另一种可能实现方式中，所述群组类型识别模型的训练过程包括：
获取样本群组的样本类型、样本图的样本图结构特征和所述样本目标图中多个样本用户节点的样本用户特征，所述样本用户节点为所述样本用户标识对应的节点，所述样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的；
调用所述群组类型识别模型，基于所述样本图结构特征和所述多个样本用户节点的样本用户特征，识别所述样本群组的预测类型；
根据所述样本类型和所述预测类型之间的差异，训练所述群组类型识别模型。
在另一种可能实现方式中，所述第一筛选网络，用于：
将所述多个第一用户节点的数量与参考比例相乘，得到参考数量；
将所述多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为所述第二用户节点。
在另一种可能实现方式中，图结构特征包括多个用户节点中任两个用户节点之间的关联度，所述特征获取模块，用于：
获取所述目标群组中任两个用户标识的共同出现次数，所述共同出现次数是指在多个参考时间段内，基于所述任两个用户标识共同在所述目标群组中发布内容的次数；
基于所述共同出现次数，确定所述任两个用户标识之间的关联度，所述关联度特征与所述共同出现次数呈正相关关系。
在另一种可能实现方式中，用户特征包括用户行为特征和用户属性特征，所述特征获取模块，用于：
获取用户社交网络，所述用户社交网络包括已注册的多个用户标识；
根据所述用户社交网络，获取所述多个用户标识的用户行为特征；
根据所述多个用户标识对应的用户画像信息，获取所述多个用户标识的用户属性特征。
另一方面，提供了一种计算机设备，所述计算机设备包括处理器和存储器，所述存储器中存储有至少一条计算机程序，所述至少一条计算机程序由所述处理器加载并执行，以实现如上述方面所述的群组类型识别方法中所执行的操作。
另一方面，提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有至少一条计算机程序，所述至少一条计算机程序由处理器加载并执行，以实现如上述方面所述的群组类型识别方法中所执行的操作。
另一方面，提供了一种计算机程序产品或计算机程序，所述计算机程序产品或所述计算机程序包括计算机程序代码，所述计算机程序代码存储在计算机可读存储介质中，计算机设备的处理器从计算机可读存储介质读取所述计算机程序代码，处理器执行所述计算机程序代 码，使得所述计算机设备实现如上述方面所述的群组类型识别方法中所执行的操作。
本申请实施例提供的技术方案带来的有益效果至少包括：
本申请实施例提供的方法，获取每个第一用户节点的注意力参数时，考虑图结构特征和用户节点特征，与现有技术中仅获取用户信息相比，增多了信息量，使得到的注意力参数能够更加准确地反映用户节点在图结构中的重要程度，从而在根据参考注意力参数对多个第一用户节点进行筛选时，能够准确选取出较为重要的用户节点，根据这些重要的用户节点的用户特征和图结构特征，识别目标群组的群组类型，以提高识别准确率，同时将不重要的用户节点丢弃，以减少处理的数据量，提高处理速度。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请实施例的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种群组类型识别方法的流程图；
图2是本申请实施例提供的一种群组类型识别模型的结构示意图；
图3是本申请实施例提供的另一种群组类型识别模型的结构示意图；
图4是本申请实施例提供的另一种群组类型识别模型的结构示意图；
图5是本申请实施例提供的另一种群组类型识别方法的流程图；
图6是本申请实施例提供的另一种群组类型识别模型的结构示意图；
图7是本申请实施例提供的一种群组类型识别装置的结构示意图；
图8是本申请实施例提供的另一种群组类型识别装置的结构示意图；
图9是本申请实施例提供的一种终端的结构示意图；
图10是本申请实施例提供的一种服务器的结构示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请实施方式作进一步地详细描述。
可以理解，本申请所使用的术语“第一”、“第二”等可在本文中用于描述各种概念，但除非特别说明，这些概念不受这些术语限制。这些术语仅用于将一个概念与另一个概念区分。举例来说，在不脱离本申请的范围的情况下，可以将第一用户节点称为第二用户节点，将第二用户节点称为第一用户节点。
本申请所使用的术语“至少一个”、“多个”、“每个”、“任一”等，至少一个包括一个、两个或两个以上，多个包括两个或两个以上，每个是指对应的多个中的每一个，任一是指多个中的任意一个。举例来说，多个用户节点包括3个用户节点，而每个用户节点是指这3个用户节点中的每一个用户节点，任一是指这3个用户节点中的任意一个，可以是第一个，可以是第二个，也可以是第三个。
现有技术中，根据群组中的多个用户标识对应的用户信息，确定该群组的类型。其中，用户信息包括用户头像、用户标签、年龄、性别等信息。但是，这种基于用户信息来确定群组类型的方式，所依据的数据量较少，导致确定的群组类型准确率较低。
本申请实施例提供的群组类型识别方法，应用于群组类型识别场景下。例如，在即时通信应用中，出于对用户隐私及用户财产安全的考虑，采用本申请实施例提供的群组类型识别方法，识别目标群组是否是特定群组，如果确定目标群组是特定群组，则对目标群组进行封群处理或其他处理，以避免对用户造成损失。
本申请实施例提供的方法由计算机设备执行，该计算机设备为终端或服务器。可选地，终端为便携式、袖珍式、手持式等多种类型的终端，如手机、计算机、平板电脑等。服务器是独立的物理服务器，或者是多个物理服务器构成的服务器集群或者分布式系统，或者是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、CDN(Content Delivery Network，内容分发网络)、以及大数据和人工智能平台等基础云计算服务的云服务器。
图1是本申请实施例提供的一种群组类型识别方法的流程图。本申请实施例的执行主体为计算机设备。参见图1，该方法包括以下步骤：
101、获取第一目标图的第一图结构特征和第一目标图中多个第一用户节点的第一用户特征。
本申请实施例中，以包括多个用户标识的目标群组为例进行说明。
其中，第一用户节点为目标群组中的用户标识对应的节点，第一目标图为根据多个第一用户节点之间的关联关系构建的。
在一种可能实现方式中，在多个第一用户节点为目标群组中的全部用户标识对应的用户节点的情况下，即多个第一用户节点是未进行筛选的，则第一目标图为初始目标图；在多个第一用户节点为目标群组中的部分用户标识对应的用户节点的情况下，即多个第一用户节点已经是筛选之后得到的，则第一目标图为筛选之后得到的目标图。
其中，第一图结构特征用于描述第一目标图，该第一目标图中包括多个第一用户节点以及该多个第一用户节点之间的连接线，任两个第一用户节点之间的连接线表示该任两个第一用户节点之间具有关联关系。第一用户特征用于描述用户标识对应的用户，该第一用户特征包括用户行为特征或用户属性特征中的至少一种。
102、基于第一图结构特征和多个第一用户特征，获取第一目标图中每个第一用户节点的注意力参数。
其中，注意力参数表示第一用户节点在第一目标图中的重要程度，也即是表示该第一用户节点对应的用户标识在目标群组中的重要程度。用户标识在目标群组中的重要程度与注意力参数呈正相关关系。例如，对于目标群组中的群主、管理员等用户来说，在目标群组中较为重要，这些用户对应的用户节点的注意力参数也会较大。
103、从多个第一用户节点中选取多个第二用户节点，多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数。
本申请实施例中，对多个第一用户节点进行筛选，从多个第一用户节点中选取注意力参数较大的多个第二用户节点，后续根据这些注意力参数较大的第二用户节点来进行处理，从而在处理过程中丢弃一些不重要的信息，保证在减少数据量的同时，减少对重要信息的损失。
104、基于多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别目标群组的群组类型。
其中，第二目标图是根据多个第二用户节点之间的关联关系构建的，第二图结构特征用于描述该第二目标图。第二目标图是第一目标图的子图，该第二目标图中包括多个第二用户节点以及该多个第二用户节点之间的连接线，第二目标图中多个第二用户节点之间的连接线，与该多个第二用户节点在第一目标图中的连接线相同。
其中，群组类型是指目标群组所属的类型，根据不同的划分标准，能够划分出不同的群组类型。例如，根据目标群组中的用户标识对应的用户的职业，划分工作群组和非工作群组；根据目标群组中的用户标识对应的用户之间是否是亲戚关系，划分家庭群组和非家庭群组；根据目标群组中的用户标识对应的用户在目标群组中的行为是否属于特定行为，划分特定群组和非特定群组。例如，特定行为包括涉及色情、赌博、诈骗等行为。
本申请实施例提供的方法，考虑图结构特征和用户节点特征，来获取每个第一用户节点 的注意力参数，与现有技术中仅获取用户信息相比，增多了信息量，使得到的注意力参数能够更加准确地反映用户节点在图结构中的重要程度，从而在根据参考注意力参数对多个第一用户节点进行筛选时，能够准确选取出较为重要的用户节点，根据这些重要的用户节点的用户特征和图结构特征，识别目标群组，以提高识别准确率，同时将不重要的用户节点丢弃能够减少处理的数据量，提高处理速度。
在一种可能实现方式中，计算机设备调用群组类型识别模型，来识别目标群组的群组类型，下面先对群组类型识别模型的模型结构进行说明。
图2是本申请实施例提供的一种群组类型识别模型的结构示意图。参见图2，该群组类型识别模型包括输入网络201、第一注意力网络202、第一筛选网络203和识别网络204。其中，输入网络201与第一注意力网络202连接，第一注意力网络202与第一筛选网络203连接，第一筛选网络203与识别网络204连接。
其中，输入网络201用于获取输入的图结构特征和用户特征，第一注意力网络202用于获取每个用户节点的注意力参数，第一筛选网络203用于根据获取的注意力参数，筛选出重要的用户节点，识别网络204用于根据筛选出的用户节点的用户特征以及对应的图结构特征来识别群组类型。
在一种可能实现方式中，参见图3，群组类型识别模型还包括第一卷积网络205、第二注意力网络206、第二筛选网络207和拼接网络208。其中，第一卷积网络205与第一筛选网络203和第二注意力网络206连接，第二注意力网络206与第二筛选网络207连接，第二筛选网络207与识别网络204连接，拼接网络208与第一筛选网络203、第二筛选网络207和识别网络204连接。
其中，第一卷积网络205用于对第一筛选网络203筛选出的比较重要的用户节点的用户特征进行进一步处理，第二注意力网络206用于获取筛选出的每个用户节点的注意力参数，第二筛选网络207用于根据获取的注意力参数，对筛选出的用户节点进行进一步筛选，拼接网络208用于拼接第一筛选网络203筛选出的用户节点的用户特征与第二筛选网络207再次筛选出的用户节点的用户特征。
上述可能实现方式中是以对用户节点进行两次筛选为例进行说明，在另一种可能实现方式中，能够对用户节点进行三次、四次甚至更多次数的筛选，从而获取更多的信息量。例如，参见图4，采用三个注意力网络、三个筛选网络对用户节点进行三次筛选。其中，拼接网络用于拼接三个筛选网络筛选出的用户节点的用户特征。
另外，在一种可能实现方式中，在第一注意力网络之前还包括卷积网络，即对输入的用户特征和图结构特征先进行卷积处理，第一注意力网络根据卷积处理后的用户特征和图结构特征，获取注意力参数。
下面通过图5所示的实施例，对调用群组类型识别模型来识别目标群组的类型进行说明。
图5是本申请实施例提供的一种群组类型识别方法的流程图。本申请实施例的执行主体为计算机设备。参见图5，该方法包括以下步骤：
501、根据目标群组中的多个用户标识之间的关联关系构建第一目标图。
本申请实施例以第一目标图为最初构建的目标图为例进行说明。
本申请实施例中，根据目标群组中的多个用户标识构建对应的用户节点，再根据多个用户标识之间的关联关系，将多个用户节点连接在一起，构成第一目标图，该第一目标图为同构图，即第一目标图中仅包括用户节点这一种类型的节点，且用户节点之间根据同种类型的关联关系进行连接，也即是第一目标图中的连接线为同一类型的连接线。
其中，任两个用户节点之间的关联关系根据该任两个用户节点之间的关联度特征确定，该关联度特征表示任两个用户节点对应的用户之间的亲密程度。
在一种可能实现方式中，获取目标群组中任两个用户标识的共同出现次数，根据共同出 现次数，确定任两个用户标识之间的关联度特征。其中，关联度特征可称为关联度，共同出现次数是指在多个参考时间段内，基于任两个用户标识共同在目标群组中发布内容的次数，关联度与共同出现次数呈正相关关系，即共同出现次数越大，两个用户标识之间的关联度越大，共同出现次数越小，两个用户标识之间的关联度越小。可选地，直接将共同出现次数作为关联度。
例如，参考时间段为5分钟，对于任两个用户，确定这两个用户在5分钟内是否都在目标群组中发言，在这两个用户都发言的情况下，这两个用户对应的共同出现次数加1，在只有一个用户发言的情况下，或者在两个用户都没有发言的情况下，共同出现次数不增加。其中，两个用户共同在目标群组中进行发言，则表示这两个用户之间交流较多，两个用户之间比较亲密，因此能够根据共同出现次数来确定关联度。
在一种可能实现方式中，考虑到有可能存在偶然情况会使两个用户同时在目标群组中发言，因此还需要确定共同出现次数是否小于参考次数，响应于共同出现次数小于参考次数，将共同出现次数设置为0，从而避免偶然情况。例如，目标群组中的群主发布一条消息，该目标群组中的除群主之外的用户可能都会回复这条消息，此时目标群组中的很多用户都会同时发言，但是这些同时发言的用户之间可能关系并不亲密。
需要说明的是，本申请实施例中仅是以第一目标图为同构图为例进行说明，在另一实施例中，第一目标图为异构图，例如，第一目标图中包括用户节点及用户类型节点，用户节点之间根据同种类型的关联关系进行连接，用户节点与用户类型节点之间根据用户节点对应的用户所属的类型进行连接。
502、获取第一目标图的第一图结构特征和第一目标图中多个第一用户节点的第一用户特征。
其中，第一图结构特征表示该第一目标图中的多个用户节点及该多个用户节点之间的关联关系，第一图结构特征包括多个用户节点中任两个用户节点之间的关联度。第一用户特征包括用户行为特征和用户属性特征，用户行为特征表示用户的历史行为，用户属性特征表示用户自身的属性。
在一种可能实现方式中，获取用户社交网络，根据该用户社交网络，获取目标群组中的多个用户标识的用户行为特征。其中，用户社交网络包括已注册的多个用户标识。
可选地，采用图嵌入方式，根据用户社交网络获取用户标识的用户行为特征。其中，图嵌入用于将图中的每个节点表示为低维空间的一个稠密向量，将得到的该稠密向量作为节点的特征信息，图嵌入的核心思想是保留图结构的内在结构属性，即在向量空间中保持图中连接的节点彼此靠近。例如，图嵌入的方法包括DeepWalk(一种生成网络中节点表示的方法)以及Node2Vec(一种生成网络中节点向量的模型)等节点嵌入方法。
以Deepwalk节点嵌入方法为例，根据用户社交网络，从图中的每个用户节点出发，根据用户社交网络以及用户连线权重，随机游走多条轨迹，将全部游走出的轨迹作为语料库输入到word2vec词向量嵌入模型，通过word2vec词向量嵌入模型进行处理，最终得到每个用户节点的用户行为特征。
在一种可能实现方式中，根据目标群组中的多个用户标识对应的用户画像信息，获取目标群组中的多个用户标识的用户属性特征。其中，用户画像信息包括用户头像、用户发布的动态信息、用户年龄、性别、用户所处的地理位置等信息。
在一种可能实现方式中，采用向量表示第一用户特征，采用矩阵表示多个第一用户特征，即将多个第一用户特征拼接在一起，构成一个矩阵。例如，矩阵中的某一行或者某一列表示一个第一用户节点的第一用户特征。
在一种可能实现方式中，采用矩阵表示第一图结构特征，矩阵中的每个位置的元素表示对应的两个用户节点之间的关联关系。例如，矩阵中第三行第四列的元素表示第三个用户节点和第四个用户节点之间的关联关系。例如，采用下述公式获取任两个第一用户节点之间的 关联关系：
A ij＝log(C ij)；
其中，A ij表示第i个第一用户节点与第j个用户节点之间的关联关系，C ij表示第i个第一用户节点对应的用户标识与第j个用户节点对应的用户标识的共同出现次数。
503、调用第一注意力网络，基于第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数。
第一注意力网络为至少一个GNN(Graph Neural Network，图神经网络)。采用下述任一种公式，获取注意力参数：
Z＝σ(GNN(X，A))；
Z＝σ(GNN(X，A+A 2))；
Z＝σ(GNN 2(σ(GNN 1(X，A))，A))；
Figure PCTCN2021141553-appb-000001
其中，Z表示注意力参数，X表示多个第一用户特征，A表示第一图结构特征，σ为参考数值，GNN(·)表示对第一用户特征和第一图结构特征进行卷积处理，m表示第m个GNN，M表示GNN的数量。其中，Z、X和A为矩阵。
上述第一个和第二个公式中使用了一个GNN，上述第三个公式中使用了两个GNN，上述第四个公式中使用了M个GNN。上述第三个公式中两个GNN依次连接，即先采用第一个GNN进行一次处理，再采用第二个GNN在第一次处理的基础上进行第二次处理，得到注意力参数。上述第四个公式中M个GNN分别对第一用户特征和第一图结构特征进行处理，对处理得到的M个注意力参数求平均，得到最终的注意力参数。
504、调用第一筛选网络，从多个第一用户节点中选取多个第二用户节点，根据多个第二用户节点之间的关联关系构建第二目标图。
其中，多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数。
在一种可能实现方式中，调用第一筛选网络，将多个第一用户节点的数量与参考比例相乘，得到参考数量，将多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为第二用户节点。选取出多个第二用户节点之后，根据选取出的多个第二用户节点之间的关联关系构成第二目标图。
可选地，采用下述公式来获取多个第二用户节点的第一用户特征和第二目标图的第二图结构特征：
X′＝X idx，：；A l＝A idx，idx；
其中，X′表示多个第二用户节点的第一用户特征，X idx，：表示从多个第一用户节点的第一用户特征中选取第二用户节点的第一用户特征，A l表示第二图结构特征，A idx，idx表示从第一图结构特征中选取出多个第二用户节点对应的关联关系。
可选地，考虑到注意力参数的大小，采用下述公式对多个第二用户节点的第一用户特征进行调整，得到调整后的第一用户特征：
X l＝X′⊙Z；
其中，X l表示调整后的第一用户特征，Z表示多个第二用户节点的注意力参数，⊙表 示按位乘积，即将每个第二用户节点的第一用户特征与注意力参数相乘。
需要说明的是，本申请实施例仅是以对用户节点进行多次筛选为例进行说明，在另一实施例中，在仅是对用户节点进行一次筛选的情况下，选取出多个第二用户节点之后，直接基于多个第二用户节点的用户特征和构建第二目标图的第二图结构特征，来识别目标群组的群组类型，不再执行后续步骤。
505、调用第一卷积网络，根据第二目标图的第二图结构特征，调整多个第二用户节点的第一用户特征，得到多个第二用户节点的第二用户特征。
第一卷积网络为GCN(Graph Convolutional Networks，图卷积网络)，采用下述公式来确定卷积处理后的第二用户特征：
X l+1＝σ(A lX lW l+1)；
其中，X l+1表示多个第二用户节点的第二用户特征，X l表示多个第二用户节点的第一用户特征，A l表示第二图结构特征，W l+1表示第一卷积网络中的模型参数，σ为参考数值。
506、调用第二注意力网络，基于第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数。
507、调用第二筛选网络，从多个第二用户节点中选取多个第三用户节点，根据多个第三用户节点之间的关联关系构建第三目标图。
步骤506-步骤507的实施方式与上述步骤503-步骤504的实施方式同理，在此不再赘述。
508、调用识别网络，基于多个第二用户节点的第一用户特征、第二图结构特征、多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别目标群组的群组类型。
在一种可能实现方式中，为了减小识别网络处理的数据量，先融合多个第二用户节点的第一用户特征和第二图结构特征，得到第一融合特征；融合多个第三用户节点的第二用户特征和第三图结构特征，得到第二融合特征；调用识别网络，基于第一融合特征和第二融合特征，识别目标群组的群组类型。
在一种可能实现方式中，根据第二用户节点的数量，对多个第二用户节点的第一用户特征和第二图结构特征进行求平均处理，即基于多个第二用户节点的第一用户特征和第二图结构特征求均值，得到多个第二用户节点对应的平均用户特征；拼接平均用户特征与多个第二用户节点的第一用户特征中的最大用户特征，得到第一融合特征。其中，由于第二图结构特征包含多个第二用户节点之间的关联关系，对于任一第二用户节点来说，能够从该第二图结构特征中确定该第二用户节点与其他第二用户节点之间的关联关系，则在求取均值时，实际上是将第二用户节点的第一用户特征和该第二用户节点与其他第二用户节点之间的关联关系作为一个整体，对多个第二用户节点的第一用户特征和多个关联关系求均值。
例如，采用下述公式获取第一融合特征：
Figure PCTCN2021141553-appb-000002
其中，s表示第一融合特征，N表示第二用户节点的数量，x i表示第i个第二用户节点的第一用户特征和第i个第二用户节点与其他第二用户节点之间的关联关系，||表示拼接||前面的特征和||后面的特征。
同理，对于第三用户节点的第二用户特征和第三图结构特征，根据第三用户节点的数量，对多个第三用户节点的第二用户特征和第三图结构特征进行求平均处理，即基于多个第三用户节点的第二用户特征和第三图结构特征求均值，得到多个第三用户节点对应的平均用户特征；拼接平均用户特征与多个第三用户节点的第二用户特征中的最大用户特征，得到第二融合特征。
在一种可能实现方式中，拼接第一融合特征和第二融合特征，得到目标群组对应的拼接特征，基于该拼接特征，识别目标群组对应的群组类型。其中，拼接特征即为表示目标群组的特征。
在一种可能实现方式中，参见图6所示的模型结构示意图，群组类型识别模型还包括第一融合网络、第二融合网络和拼接网络，即调用第一融合网络，融合多个第二用户节点的第一用户特征和第二图结构特征，得到第一融合特征；调用第二融合网络，融合多个第三用户节点的第二用户特征和第三图结构特征，得到第二融合特征；调用拼接网络，拼接第一融合特征和第二融合特征，得到目标群组对应的拼接特征；调用识别网络，基于拼接特征，识别目标群组的群组类型。
以确定目标群组是否为特定群组为例进行说明，在一种可能实现方式中，识别网络为分类器，该分类器包括多层感知机，识别网络的输出为0或1，在识别网络的输出为0的情况下，表示目标群组不是特定群组，在识别网络的输出为1的情况下，表示目标群组是特定群组。例如，采用下述公式确定识别网络的输出值：
Figure PCTCN2021141553-appb-000003
其中，
Figure PCTCN2021141553-appb-000004
表示识别网络的输出值，E表示拼接特征，MLP(·)表示采用MLP(Multi-Layer Perceptron，多层感知机)来对拼接特征进行处理。
在另一种可能实现方式中，识别网络的输出为概率，在输出的概率大于参考概率的情况下，表示目标群组为特定群组，在输出的概率不大于参考概率的情况下，表示目标群组不是特定群组。
需要说明的是，图5所示的实施例仅是以调用群组类型识别模型来识别目标群组的群组类型为例进行说明，在另一实施例中，计算机设备能够直接基于第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，从多个第一用户节点中选取多个第二用户节点，根据多个第二用户节点之间的关联关系构建第二目标图，根据第二目标图的第二图结构特征，调整多个第二用户节点的第一用户特征，得到多个第二用户节点的第二用户特征，基于第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数，从多个第二用户节点中选取多个第三用户节点，根据多个第三用户节点之间的关联关系构建第三目标图，基于多个第二用户节点的第一用户特征、第二图结构特征、多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别目标群组的群组类型。
本申请实施例提供的方法，获取每个第一用户节点的注意力参数时，考虑图结构特征和用户节点特征，与现有技术中仅获取用户信息相比，增多了信息量，使得到的注意力参数能够更加准确地反映用户节点在图结构中的重要程度，从而在根据参考注意力参数对多个第一用户节点进行筛选时，能够准确选取出较为重要的用户节点，根据这些重要的用户节点的用户特征和图结构特征，识别目标群组的群组类型，以提高识别准确率，同时将不重要的用户节点丢弃，以减少处理的数据量，提高处理速度。
并且，本申请实施例中，对用户节点进行多次筛选，得到不同层次的用户特征和图结构特征，在识别目标群组时，考虑不同层次的用户特征和图结构特征，进一步提高了识别准确率。
在一种可能实现方式中，在采用群组类型识别模型之前，需要先训练群组类型识别模型，群组类型识别模型的训练过程包括：获取样本群组的样本类型、样本图的样本图结构特征和样本目标图中多个样本用户节点的样本用户特征；调用群组类型识别模型，基于样本图结构特征和多个样本用户节点的样本用户特征，识别样本群组的预测类型；基于样本类型和预测类型之间的差异，训练群组类型识别模型。其中，样本用户节点为样本用户标识对应的节点，样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的。
例如，采用平方误差来训练群组类型识别模型：
Figure PCTCN2021141553-appb-000005
其中，
Figure PCTCN2021141553-appb-000006
表示预测类型，Y表示样本类型，L i表示预测类型与样本类型之间的差异。
可选地，调用群组类型识别模型，识别得到目标群组的群组类型之后，能够采用目标群组继续训练该群组类型识别模型。
需要说明的是，上述训练过程仅是以一次训练为例，在另一实施例能够对群组类型识别模型进行多次迭代训练。
在一种可能实现方式中，对于确定目标群组是否是特定群组这一应用场景，现有技术中，采用关键词过滤技术，确定用户发布的内容中是否包括特定词汇，如果包括特定词汇则认为该目标群组属于特定群组，但是如果用户采用其他非特定词汇代替对应的特定词汇，则采用关键词过滤技术无法检测出来，也就不能够确定是否是特定词汇，对目标群组的识别准确率较低。在另一种现有技术中，采用用户举报的方式，即用户对目标群组进行举报后，由技术人员再进行人工复核，确定目标群组是否属于特定群组，但是这种方式依赖于用户的举报，受到用户举报行为的影响，对目标群组的识别准确率也较低。本申请实施例提供的方式与上述两种现有技术相比较，不受用户所使用的词汇和用户举报行为的影响，能够直接根据目标群组中多个用户节点之间的关联关系，以及用户节点的用户特征，识别目标群组的群组类型，提高了识别准确率。
在另一种现有技术中，采用池化函数来读取目标群组中的全部用户节点的信息，但是当用户节点较多时，采用池化函数难以读出全部的信息，会导致丢失大量信息，并且用户节点越多，处理速度也会越慢。而本申请中能够根据注意力参数对用户节点进行筛选，只需对筛选出的用户节点的信息进行读出即可，且根据注意力参数的大小能够保证保留重要的节点的信息，不会导致丢失大量信息，且在保证信息量的同时，提高了处理速度。
图7是本申请实施例提供的一种群组类型识别装置的结构示意图。参见图7，该装置包括：
特征获取模块701，用于获取第一目标图的第一图结构特征和第一目标图中多个第一用户节点的第一用户特征，第一用户节点为目标群组中的用户标识对应的节点，第一目标图为根据多个第一用户节点之间的关联关系构建的；
第一注意力获取模块702，用于基于第一图结构特征和多个第一用户特征，获取第一目标图中每个第一用户节点的注意力参数，注意力参数表示第一用户节点在第一目标图中的重要程度；
第一筛选模块703，用于根据得到的多个注意力参数，从多个第一用户节点中选取出多个第二用户节点，多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
类型识别模块704，用于基于多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别目标群组的群组类型，第二目标图为根据多个第二用户节点之间的关联关系构建的。
本申请实施例提供的装置，获取每个第一用户节点的注意力参数时，考虑图结构特征和用户节点特征，与现有技术中仅获取用户信息相比，增多了信息量，使得到的注意力参数能够更加准确地反映用户节点在图结构中的重要程度，从而在根据参考注意力参数对多个第一用户节点进行筛选时，能够准确选取出较为重要的用户节点，根据这些重要的用户节点的信息，识别目标群组的群组类型，以提高识别准确率，同时将不重要的用户节点丢弃，以减少处理的数据量，提高处理速度。
在一种可能实现方式中，参见图8，该装置还包括：
特征调整模块705，用于基于第二图结构特征，调整多个第二用户节点的第一用户特征，得到多个第二用户节点的第二用户特征；
第二注意力获取模块706，用于基于第二图结构特征和多个第二用户特征进行处理，获取每个第二用户节点的注意力参数；
第二筛选模块707，用于从多个第二用户节点中选取多个第三用户节点，多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在另一种可能实现方式中，类型识别模块704，用于基于多个第二用户节点的第一用户特征、第二图结构特征、多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别目标群组的群组类型，第三目标图为根据多个第三用户节点之间的关联关系构建的。
在另一种可能实现方式中，参见图8，类型识别模块704，包括：
第一融合单元7041，用于融合多个第二用户节点的第一用户特征和第二图结构特征，得到第一融合特征；
第二融合单元7042，用于融合多个第三用户节点的第二用户特征和第三图结构特征，得到第二融合特征；
类型识别单元7043，用于基于第一融合特征和第二融合特征，识别目标群组的群组类型。
在另一种可能实现方式中，参见图8，第一融合单元7041，用于：
基于多个第二用户节点的第一用户特征和第二图结构特征求均值，得到平均用户特征；
拼接平均用户特征与多个第二用户节点的第一用户特征中的最大用户特征，得到第一融合特征。
在另一种可能实现方式中，参见图8，类型识别单元7043，用于：
拼接第一融合特征和第二融合特征，得到目标群组对应的拼接特征；
基于拼接特征，识别目标群组的群组类型。
在另一种可能实现方式中，参见图8，群组类型识别模型包括第一注意力网络、第一筛选网络和识别网络，
第一注意力获取模块702，用于调用第一注意力网络，基于第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数；
第一筛选模块703，用于调用第一筛选网络，从多个第一用户节点中选取多个第二用户节点；
类型识别模块704，用于调用识别网络，基于多个第二用户节点的第一用户特征和第二图结构特征，识别目标群组的群组类型。
在另一种可能实现方式中，群组类型识别模型还包括第一卷积网络、第二注意力网络和第二筛选网络，参见图8，该装置还包括：
特征调整模块705，用于调用第一卷积网络，基于第二图结构特征，调整多个第二用户节点的第一用户特征，得到多个第二用户节点的第二用户特征；
第二注意力获取模块706，用于调用第二注意力网络，基于第二图结构特征和多个第二用户特征，获取第二目标图中每个第二用户节点的注意力参数；
第二筛选模块707，用于调用第二筛选网络，从多个第二用户节点中选取多个第三用户节点，多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在另一种可能实现方式中，类型识别模块704，用于调用识别网络，基于对多个第二用户节点的第一用户特征、第二图结构特征、多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别目标群组的群组类型，第三目标图为根据多个第三用户节点之间的关联关系构建的。
在另一种可能实现方式中，群组类型识别模型还包括第一融合网络和第二融合网络，参见图8，类型识别模块704，包括：
第一融合单元7041，用于调用第一融合网络，融合多个第二用户节点的第一用户特征和第二图结构特征，得到第一融合特征；
第二融合单元7042，用于调用第二融合网络，融合多个第三用户节点的第二用户特征和第三图结构特征，得到第二融合特征；
类型识别单元7043，用于调用识别网络，基于第一融合特征和第二融合特征，识别目标群组的群组类型。
在另一种可能实现方式中，群组类型识别模型还包括拼接网络，参见图8，类型识别单元7043，用于：
调用拼接网络，拼接第一融合特征和第二融合特征，得到目标群组对应的拼接特征；
调用识别网络，基于拼接特征，识别目标群组的群组类型。
在另一种可能实现方式中，群组类型识别模型的训练过程包括：
获取样本群组的样本类型、样本图的样本图结构特征和样本目标图中多个样本用户节点的样本用户特征，样本用户节点为样本用户标识对应的节点，样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的；
调用群组类型识别模型，基于样本图结构特征和多个样本用户节点的样本用户特征，识别样本群组的预测类型；
根据样本类型和预测类型之间的差异，训练群组类型识别模型。
在另一种可能实现方式中，参见图8，第一筛选网络703，用于：
将多个第一用户节点的数量与参考比例相乘，得到参考数量；
将多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为第二用户节点。
在另一种可能实现方式中，图结构特征包括多个用户节点中任两个用户节点之间的关联度特征，参见图8，特征获取模块701，用于：
获取目标群组中任两个用户标识的共同出现次数，共同出现次数是指在多个参考时间段内，基于任两个用户标识共同在目标群组中发布内容的次数；
根据共同出现次数，确定任两个用户标识之间的关联度，关联度与共同出现次数呈正相关关系。
在另一种可能实现方式中，用户特征包括用户行为特征和用户属性特征，参见图8，特征获取模块701，用于：
获取用户社交网络，用户社交网络包括已注册的多个用户标识；
根据用户社交网络，获取多个用户标识的用户行为特征；
根据多个用户标识对应的用户画像信息，获取多个用户标识的用户属性特征。
上述所有可选技术方案，可以采用任意结合形成本申请的可选实施例，在此不再一一赘述。
需要说明的是：上述实施例提供的群组类型识别装置在识别群组类型时，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将计算机设备的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。另外，上述实施例提供的群组类型识别装置与群组类型识别方法实施例属于同一构思，其具体实现过程详见方法实施例，这里不再赘述。
本申请实施例还提供了一种计算机设备，该计算机设备包括处理器和存储器，存储器中存储有至少一条计算机程序，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
基于所述第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
从所述多个第二用户节点中选多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：基于所述多个第二用户节点的第一用户特征和所述第二图结构特征求均值，得到平均用户特征；
拼接所述平均用户特征与所述多个第二用户节点的第一用户特征中的最大用户特征，得到所述第一融合特征。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
基于所述拼接特征，识别所述目标群组的群组类型。
在一种可能实现方式中，群组类型识别模型包括第一注意力网络、第一筛选网络和识别网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一注意力网络，基于所述第一图结构特征和所述多个第一用户特征，获取所述每个第一用户节点的注意力参数；
所述从所述多个第一用户节点中选取多个第二用户节点，包括：
调用所述第一筛选网络，从所述多个第一用户节点中选取所述多个第二用户节点；
所述基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，包括：
调用所述识别网络，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征，识别所述目标群组的群组类型。
在一种可能实现方式中，所述群组类型识别模型还包括第一卷积网络、第二注意力网络和第二筛选网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一卷积网络，基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
调用所述第二注意力网络，基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
调用所述第二筛选网络，从所述多个第二用户节点中选取多个第三用户节点，所述多个 第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述识别网络，基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在一种可能实现方式中，所述群组类型识别模型还包括第一融合网络和第二融合网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一融合网络，融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
调用所述第二融合网络，融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
调用所述识别网络，基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在一种可能实现方式中，所述群组类型识别模型还包括拼接网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述拼接网络，拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
调用所述识别网络，基于所述拼接特征，识别所述目标群组的群组类型。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取样本群组的样本类型、样本图的样本图结构特征和所述样本目标图中多个样本用户节点的样本用户特征，所述样本用户节点为所述样本用户标识对应的节点，所述样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的；
调用所述群组类型识别模型，基于所述样本图结构特征和所述多个样本用户节点的样本用户特征，识别所述样本群组的预测类型；
根据所述样本类型和所述预测类型之间的差异，训练所述群组类型识别模型。
在一种可能实现方式中，图结构特征包括多个用户节点中任两个用户节点之间的关联度，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取所述目标群组中任两个用户标识的共同出现次数，所述共同出现次数是指在多个参考时间段内，基于所述任两个用户标识共同在所述目标群组中发布内容的次数；
基于所述共同出现次数，确定所述任两个用户标识之间的关联度，所述关联度与所述共同出现次数呈正相关关系。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
将所述多个第一用户节点的数量与参考比例相乘，得到参考数量；
将所述多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为所述第二用户节点。
在一种可能实现方式中，用户特征包括用户行为特征和用户属性特征，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取用户社交网络，所述用户社交网络包括已注册的多个用户标识；
根据所述用户社交网络，获取所述多个用户标识的用户行为特征；
根据所述多个用户标识对应的用户画像信息，获取所述多个用户标识的用户属性特征。
可选地，该计算机设备提供为终端。图9是本申请实施例提供的一种终端900的结构示意图。终端900包括有：处理器901和存储器902。
处理器901可以包括一个或多个处理核心，比如4核心处理器、8核心处理器等。处理器901也可以包括主处理器和协处理器，主处理器是用于对在唤醒状态下的数据进行处理的处理器，也称CPU(Central Processing Unit，中央处理器)；协处理器是用于对在待机状态下 的数据进行处理的低功耗处理器。在一些实施例中，处理器901可以集成有GPU(Graphics Processing Unit，图像处理器)，GPU用于负责显示屏所需要显示的内容的渲染和绘制。
存储器902可以包括一个或多个计算机可读存储介质，该计算机可读存储介质可以是非暂态的。在一些实施例中，存储器902中的非暂态的计算机可读存储介质用于存储至少一条计算机程序，该至少一条计算机程序用于被处理器901所执行以实现本申请中方法实施例提供的群组类型识别方法。
在一些实施例中，终端900还可选包括有：外围设备接口903和至少一个外围设备。处理器901、存储器902和外围设备接口903之间可以通过总线或信号线相连。各个外围设备可以通过总线、信号线或电路板与外围设备接口903相连。具体地，外围设备包括：射频电路904、显示屏905、摄像头组件906、音频电路907和电源908中的至少一种。
外围设备接口903可被用于将I/O(Input/Output，输入/输出)相关的至少一个外围设备连接到处理器901和存储器902。在一些实施例中，处理器901、存储器902和外围设备接口903被集成在同一芯片或电路板上；在一些其他实施例中，处理器901、存储器902和外围设备接口903中的任意一个或两个可以在单独的芯片或电路板上实现，本实施例对此不加以限定。
射频电路904用于接收和发射RF(Radio Frequency，射频)信号，也称电磁信号。射频电路904通过电磁信号与通信网络以及其他通信设备进行通信。射频电路904将电信号转换为电磁信号进行发送，或者，将接收到的电磁信号转换为电信号。
显示屏905用于显示UI(User Interface，用户界面)。该UI可以包括图形、文本、图标、视频及其它们的任意组合。当显示屏905是触摸显示屏时，显示屏905还具有采集在显示屏905的表面或表面上方的触摸信号的能力。该触摸信号可以作为控制信号输入至处理器901进行处理。
摄像头组件906用于采集图像或视频。可选地，摄像头组件906包括前置摄像头和后置摄像头。
音频电路907可以包括麦克风和扬声器。麦克风用于采集用户及环境的声波，并将声波转换为电信号输入至处理器901进行处理，或者输入至射频电路904以实现语音通信。
电源908用于为终端900中的各个组件进行供电。电源908可以是交流电、直流电、一次性电池或可充电电池。
本领域技术人员可以理解，图9中示出的结构并不构成对终端900的限定，可以包括比图示更多或更少的组件，或者组合某些组件，或者采用不同的组件布置。
可选地，该计算机设备提供为服务器。图10是本申请实施例提供的一种服务器的结构示意图，该服务器1000可因配置或性能不同而产生比较大的差异，可以包括一个或一个以上处理器(Central Processing Units，CPU)1001和一个或一个以上的存储器1002，其中，存储器1002中存储有至少一条计算机程序，该至少一条计算机程序由处理器1001加载并执行以实现上述各个方法实施例提供的方法。当然，该服务器还可以具有有线或无线网络接口、键盘以及输入输出接口等部件，以便进行输入输出，该服务器还可以包括其他用于实现设备功能的部件，在此不做赘述。
本申请实施例还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有至少一条计算机程序，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
基于所述第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
从所述多个第二用户节点中选多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：基于所述多个第二用户节点的第一用户特征和所述第二图结构特征求均值，得到平均用户特征；
拼接所述平均用户特征与所述多个第二用户节点的第一用户特征中的最大用户特征，得到所述第一融合特征。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
基于所述拼接特征，识别所述目标群组的群组类型。
在一种可能实现方式中，群组类型识别模型包括第一注意力网络、第一筛选网络和识别网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一注意力网络，基于所述第一图结构特征和所述多个第一用户特征，获取所述每个第一用户节点的注意力参数；
所述从所述多个第一用户节点中选取多个第二用户节点，包括：
调用所述第一筛选网络，从所述多个第一用户节点中选取所述多个第二用户节点；
所述基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，包括：
调用所述识别网络，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征，识别所述目标群组的群组类型。
在一种可能实现方式中，所述群组类型识别模型还包括第一卷积网络、第二注意力网络和第二筛选网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一卷积网络，基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
调用所述第二注意力网络，基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
调用所述第二筛选网络，从所述多个第二用户节点中选取多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述识别网络，基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
在一种可能实现方式中，所述群组类型识别模型还包括第一融合网络和第二融合网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述第一融合网络，融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
调用所述第二融合网络，融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
调用所述识别网络，基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
在一种可能实现方式中，所述群组类型识别模型还包括拼接网络，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
调用所述拼接网络，拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
调用所述识别网络，基于所述拼接特征，识别所述目标群组的群组类型。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取样本群组的样本类型、样本图的样本图结构特征和所述样本目标图中多个样本用户节点的样本用户特征，所述样本用户节点为所述样本用户标识对应的节点，所述样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的；
调用所述群组类型识别模型，基于所述样本图结构特征和所述多个样本用户节点的样本用户特征，识别所述样本群组的预测类型；
根据所述样本类型和所述预测类型之间的差异，训练所述群组类型识别模型。
在一种可能实现方式中，图结构特征包括多个用户节点中任两个用户节点之间的关联度，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取所述目标群组中任两个用户标识的共同出现次数，所述共同出现次数是指在多个参考时间段内，基于所述任两个用户标识共同在所述目标群组中发布内容的次数；
基于所述共同出现次数，确定所述任两个用户标识之间的关联度，所述关联度与所述共同出现次数呈正相关关系。
在一种可能实现方式中，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
将所述多个第一用户节点的数量与参考比例相乘，得到参考数量；
将所述多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为所述第二用户节点。
在一种可能实现方式中，用户特征包括用户行为特征和用户属性特征，该至少一条计算机程序由处理器加载并执行，以实现如下步骤：
获取用户社交网络，所述用户社交网络包括已注册的多个用户标识；
根据所述用户社交网络，获取所述多个用户标识的用户行为特征；
根据所述多个用户标识对应的用户画像信息，获取所述多个用户标识的用户属性特征。
本申请实施例还提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机程序代码，该计算机程序代码存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机程序代码，处理器执行该计算机程序代码，使得计算机设备实现上述实施例的群组类型识别方法中所执行的操作。
本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也可以通过程序来指令相关的硬件完成，该程序可以存储于一种计算机可读存储介质中，上 述提到的存储介质可以是只读存储器，磁盘或光盘等。
能够理解的是，在本申请的具体实施方式中，涉及到用户标识、用户行为、用户属性数据等相关的数据，当本申请以上实施例运用到具体产品或技术中时，需要获得用户许可或者同意，且相关数据的收集、使用和处理需要遵守相关国家和地区的相关法律法规和标准。
以上仅为本申请实施例的可选实施例，并不用以限制本申请实施例，凡在本申请实施例的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (18)

  1. 一种群组类型识别方法，由计算机设备执行，所述方法包括：
    获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
    基于所述第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
    从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
    基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
  2. 根据权利要求1所述的方法，其中，所述从所述多个第一用户节点中选取多个第二用户节点之后，所述方法还包括：
    基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
    基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
    从所述多个第二用户节点中选多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
  3. 根据权利要求2所述的方法，其中，所述基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，包括：
    基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
  4. 根据权利要求3所述的方法，其中，所述基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，包括：
    融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
    融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
    基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
  5. 根据权利要求4所述的方法，其中，所述融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征，包括：
    基于所述多个第二用户节点的第一用户特征和所述第二图结构特征求均值，得到平均用户特征；
    拼接所述平均用户特征与所述多个第二用户节点的第一用户特征中的最大用户特征，得到所述第一融合特征。
  6. 根据权利要求4所述的方法，其中，所述基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型，包括：
    拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
    基于所述拼接特征，识别所述目标群组的群组类型。
  7. 根据权利要求1所述的方法，其中，群组类型识别模型包括第一注意力网络、第一筛选网络和识别网络，所述基于所述第一图结构特征和多个第一用户特征，获取每个第一用户节点的注意力参数，包括：
    调用所述第一注意力网络，基于所述第一图结构特征和所述多个第一用户特征，获取所述每个第一用户节点的注意力参数；
    所述从所述多个第一用户节点中选取多个第二用户节点，包括：
    调用所述第一筛选网络，从所述多个第一用户节点中选取所述多个第二用户节点；
    所述基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，包括：
    调用所述识别网络，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征，识别所述目标群组的群组类型。
  8. 根据权利要求7所述的方法，其中，所述群组类型识别模型还包括第一卷积网络、第二注意力网络和第二筛选网络，所述调用所述第一筛选网络，从所述多个第一用户节点中选取所述多个第二用户节点之后，所述方法还包括：
    调用所述第一卷积网络，基于所述第二图结构特征，调整所述多个第二用户节点的第一用户特征，得到所述多个第二用户节点的第二用户特征；
    调用所述第二注意力网络，基于所述第二图结构特征和多个第二用户特征，获取每个第二用户节点的注意力参数；
    调用所述第二筛选网络，从所述多个第二用户节点中选取多个第三用户节点，所述多个第三用户节点的注意力参数大于未被选取的第二用户节点的注意力参数。
  9. 根据权利要求8所述的方法，其中，所述调用所述识别网络，基于所述多个第二用户节点的第一用户特征和所述第二图结构特征，识别所述目标群组的群组类型，包括：
    调用所述识别网络，基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，所述第三目标图为根据所述多个第三用户节点之间的关联关系构建的。
  10. 根据权利要求9所述的方法，其中，所述群组类型识别模型还包括第一融合网络和第二融合网络，所述调用所述识别网络，基于所述多个第二用户节点的第一用户特征、所述第二图结构特征、所述多个第三用户节点的第二用户特征和第三目标图的第三图结构特征，识别所述目标群组的群组类型，包括：
    调用所述第一融合网络，融合所述多个第二用户节点的第一用户特征和所述第二图结构特征，得到第一融合特征；
    调用所述第二融合网络，融合所述多个第三用户节点的第二用户特征和所述第三图结构特征，得到第二融合特征；
    调用所述识别网络，基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型。
  11. 根据权利要求10所述的方法，其中，所述群组类型识别模型还包括拼接网络，所述调用所述识别网络，基于所述第一融合特征和所述第二融合特征，识别所述目标群组的群组类型，包括：
    调用所述拼接网络，拼接所述第一融合特征和所述第二融合特征，得到所述目标群组对应的拼接特征；
    调用所述识别网络，基于所述拼接特征，识别所述目标群组的群组类型。
  12. 根据权利要求7-11任一项所述的方法，其中，所述群组类型识别模型的训练过程包括：
    获取样本群组的样本类型、样本图的样本图结构特征和所述样本目标图中多个样本用户节点的样本用户特征，所述样本用户节点为所述样本用户标识对应的节点，所述样本图为根据样本群组中的多个样本用户标识之间的关联关系构建的；
    调用所述群组类型识别模型，基于所述样本图结构特征和所述多个样本用户节点的样本用户特征，识别所述样本群组的预测类型；
    根据所述样本类型和所述预测类型之间的差异，训练所述群组类型识别模型。
  13. 根据权利要求1所述的方法，其中，图结构特征包括多个用户节点中任两个用户节点之间的关联度，所述获取第一目标图的图结构特征，包括：
    获取所述目标群组中任两个用户标识的共同出现次数，所述共同出现次数是指在多个参考时间段内，基于所述任两个用户标识共同在所述目标群组中发布内容的次数；
    基于所述共同出现次数，确定所述任两个用户标识之间的关联度，所述关联度与所述共同出现次数呈正相关关系。
  14. 根据权利要求1所述的方法，其中，所述从所述多个第一用户节点中选取多个第二用户节点，包括：
    将所述多个第一用户节点的数量与参考比例相乘，得到参考数量；
    将所述多个第一用户节点的注意力参数按照从大到小的顺序排列，选取排列在前面的参考数量个注意力参数，将选取的注意力参数对应的第一用户节点作为所述第二用户节点。
  15. 根据权利要求1所述的方法，其中，用户特征包括用户行为特征和用户属性特征，获取所述第一目标图中多个第一用户节点的第一用户特征，包括：
    获取用户社交网络，所述用户社交网络包括已注册的多个用户标识；
    根据所述用户社交网络，获取所述多个用户标识的用户行为特征；
    根据所述多个用户标识对应的用户画像信息，获取所述多个用户标识的用户属性特征。
  16. 一种群组类型识别装置，其特征在于，所述装置包括：
    特征获取模块，用于获取第一目标图的第一图结构特征和所述第一目标图中多个第一用户节点的第一用户特征，所述第一用户节点为目标群组中的用户标识对应的节点，所述第一目标图为根据所述多个第一用户节点之间的关联关系构建的；
    第一注意力获取模块，用于基于所述第一图结构特征和多个第一用户特征，获取所述第一目标图中每个第一用户节点的注意力参数，所述注意力参数表示所述第一用户节点在所述第一目标图中的重要程度；
    第一筛选模块，用于从所述多个第一用户节点中选取多个第二用户节点，所述多个第二用户节点的注意力参数大于未被选取的第一用户节点的注意力参数；
    类型识别模块，用于基于所述多个第二用户节点的第一用户特征和第二目标图的第二图结构特征，识别所述目标群组的群组类型，所述第二目标图为根据所述多个第二用户节点之间的关联关系构建的。
  17. 一种计算机设备，其特征在于，所述计算机设备包括处理器和存储器，所述存储器中存储有至少一条计算机程序，所述至少一条计算机程序由所述处理器加载并执行，以实现如权利要求1至15任一权利要求所述的群组类型识别方法中所执行的操作。
  18. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有至少一条 计算机程序，所述至少一条计算机程序由处理器加载并执行，以实现如权利要求1至15任一权利要求所述的群组类型识别方法中所执行的操作。
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