Graph neural network training method and device

Abstract

The embodiment of the invention provides a graph neural network training method, and the method comprises the steps: obtaining a relation network graph, wherein each object node corresponds to a sampling probability set, and the sampling probability set comprises the sampling probability of each first-order neighbor node; performing multiple rounds of iterative updating on the graph neural networkon the basis of the relation network graph, wherein any round includes performing M-order neighbor node sampling by taking the first label node selected in the current round as a center, wherein anyi-order neighbor node sampling includes, for any first node in the sampled i-1th-order neighbor nodes, based on the current sampling probability set, sampling a plurality of neighbor nodes from a first-order neighbor node set, and classifying the neighbor nodes into an ith-order neighbor node; performing the current round of updating on the graph neural network based on the sampled neighbor nodeswithin the M order and the first service label carried by the first label node; and determining a plurality of training feedbacks corresponding to the plurality of neighbor nodes by using the updatedgraph neural network, and then updating the current sampling probability set of the first node.

Description

technical field [0001] The embodiments of this specification relate to the field of computer technology, and in particular to a training method and device for a graph neural network. Background technique [0002] The relationship network diagram is a description of the relationship between entities in the real world, and is currently widely used in various computer information processing. Generally, a relational network graph includes a set of nodes and a set of edges. Nodes represent entities in the real world, and edges represent connections between entities in the real world. For example, in a social network, people are entities, and relationships or links between people are edges. [0003] In many cases, it is hoped to analyze the topological characteristics of nodes, edges, etc. in the relational network graph, and extract effective information from them. The computing method to realize this kind of process is called graph computing. Typically, it is desired to repres...

AI Technical Summary

Problems solved by technology

Usually, the graph neural network needs to use the information of its multi-order neighbors when determining the embedding expression of a node. However, as the order increases, the number of neighbors will increase exponentially, which will bring great impact on computing resources and memory usage. big burden

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