Information pushing method and device based on graph network

Abstract

The invention relates to the technical field of computers, in particular to an information pushing method and device based on a graph network. The method comprises the steps of determining a sub-graphstructure taking the to-be-processed node as a central node in a graph network, the sub-graph structure comprising nodes connected with the to-be-processed node and a connection relationship; inputting the subgraph structure into a node feature extraction model to obtain an aggregation feature vector corresponding to a to-be-processed node output by the node feature extraction model; screening nodes matched with the to-be-processed nodes as information pushing nodes based on the aggregation feature vectors of the nodes in the graph network; and pushing the service information corresponding tothe information pushing node, so that the graph structure feature vector is added on the basis of the aggregation feature vector during training, namely, the graph structure information is added forconstraint, the convergence speed and accuracy of training are improved, and the sub-graph structure can be input during application. And the node representation accuracy and the information pushing accuracy in the graph network are improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a graph network-based information push method and device. Background technique [0002] Graph Network (GN) is the most direct tool for describing community relationship chains. For example, in business scenarios such as finance and the Internet of Things, a graph network is composed of nodes and edges, and is a generalized artificial neural network based on a graph structure. , the graph embedding algorithm is the process of mapping a graph data into a low-dimensional dense vector, and encoding the nodes, so that it can be easily applied to specific downstream tasks, such as information push, etc. In related technologies, it can be Use the unsupervised graph embedding algorithm to train the graph network, for example, the GraphSAGE algorithm, for a node, the local neighbors of the node are sampled and aggregated into features, and then combined with the characteristic...

AI Technical Summary

Problems solved by technology

[0002] Graph Network (GN) is the most direct tool for describing community relationship chains. For example, in business scenarios such as finance and the Internet of Things, a graph network is composed of nodes and edges, and is a generalized artificial neural network based on a graph structure. , the graph embedding algorithm is the process of mapping a graph data into a low-dimensional dense vector, and encoding the nodes, so that it can be easily applied to specific downstream tasks, such as information push, etc. In related technologies, it can be Use the unsupervised graph embedding algorithm to train the graph network, for example, the GraphSAGE algorithm, for a node, the local neighbors of the node are sampled and aggregated into features, and then combined with the characteristics of the node itself for learning, so that the graph network can be learned Part of the graph structure information, but in this way, the specified graph structure cannot be learned and trained, and generally only the positive and negative sample pairs are formed through the connection relationship, which easily leads to low rationality of the positive and negative samples, and only considers the adjacency relationship between nodes , also lacks a graph structure description between nodes, which reduces the training accuracy and convergence speed, thereby reducing the accuracy of node vector representation and information push accuracy

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