Data processing method and device based on graph relation network, equipment and medium
A relationship network and relationship technology, applied in the computer field, can solve problems such as reducing the accuracy of graph relationship features and failing to obtain neighbor node features.
Active Publication Date: 2020-08-28
TENCENT TECH (SHENZHEN) CO LTD
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However, in the case of using the existing technology to predict the graph relationship characteristics of a node (for example, node 2) that does not have neighbor nodes, since node 2 does not have neighbor nodes, it will not be possible to obtain the characteristics of the neighbor nodes of node 2, So that there may be a situation where the characteristics of node 2 itself are mistakenly used as the graph relationship characteristics of node 2, thereby reducing the accuracy of obtaining graph relationship characteristics
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[0085] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0086] See figure 1 , is a schematic structural diagram of a network architecture provided in an embodiment of the present application. like figure 1 As shown, the network architecture may include a server 10 and a user terminal cluster, and the user terminal cluster may include multiple user terminals, such as figure 1 As shown, it may specifically include a user terminal 100a, a user terminal 100b, a user terminal 100c, ..., a user terminal 100n.
[0087] like figure 1 As show...
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The embodiment of the invention discloses a data processing method and device based on a graph relation network, equipment and a medium, and the data processing method comprises the steps: taking an independent node in the graph relation network as a cold start node, and obtaining the attribute information of the cold start node; obtaining a node cold start model associated with the graph relationnetwork, and performing vector conversion processing on the attribute coding vector of the cold start node based on the node cold start model to obtain a target word vector feature of the cold startnode; performing cold start processing on the target word vector features based on a node cold start model to obtain target graph relationship features associated with cold start nodes; and pushing nodes matched with the cold start nodes in the graph relationship network according to the target graph relationship features. By adopting the data processing method and device based on a graph relationnetwork, the target graph relationship features of the cold start node can be directly predicted through the node cold start model, and the accuracy of obtaining the graph relationship features is improved.
Description
technical field [0001] The present application relates to the field of computer technology, and in particular to a data processing method, device, device and medium based on a graph-relational network. Background technique [0002] Currently, when the Graph Sampling and Aggregation (Graph SAmple and AggreGatE, GraphSAGE) algorithm predicts the graph relationship characteristics of a node (for example, node 1) in the graph relationship network, it needs to obtain the neighbor nodes of the node 1 to pass The characteristics of the node 1 itself and the characteristics of the neighbor nodes are used to comprehensively determine the graph relationship characteristics used to describe the node 1 . [0003] For example, taking the order of the neighbor nodes of node 1 in the graph relationship network as the second order as an example, the first-order neighbor nodes of node 1 can be represented by the corresponding second-order neighbor node features, and after merging the first-o...
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IPC IPC(8): G06F16/901
CPCG06F16/9024
Inventor 陈思宏肖万鹏鞠奇
Owner TENCENT TECH (SHENZHEN) CO LTD