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Graph neural network training method, graph neural network equipment and device and medium

A neural network and equipment technology, applied in the field of graph neural network, can solve the problems of ignoring the important role of the representation vector of the edge and not considering the influence of the representation vector of the edge, etc., to achieve the effect of improving the training effect

Inactive Publication Date: 2019-10-18
TENCENT TECH (SHENZHEN) CO LTD
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Problems solved by technology

However, the traditional neural network training method does not consider the influence of edge representation vectors on the training results, and ignores the important role of edge representation vectors in the training process.

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  • Graph neural network training method, graph neural network equipment and device and medium
  • Graph neural network training method, graph neural network equipment and device and medium
  • Graph neural network training method, graph neural network equipment and device and medium

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Embodiment Construction

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only some of the embodiments of the present disclosure, not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0031] "First", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Likewise, "comprising" or "comprises" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, and do not exclude other elements or items. Words such as "connected" or "connected" a...

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Abstract

The invention provides a graph neural network training method, graph neural network equipment and device and a medium. The graph neural network training method comprises the steps: obtaining graph structure data of a graph neural network, the graph structure data comprising representation vectors of nodes and representation vectors of edges, and the edges representing association relationships between connected nodes; utilizing the graph neural network to generate a transfer matrix based on the characterization vector of the edge, the transfer matrix representing an information transfer mode between connected nodes; determining mutual information between the representation vector of the edge and the transfer matrix; and training the graph neural network by using the mutual information.

Description

technical field [0001] The present disclosure relates to the field of graph neural networks, and in particular to a method for training graph neural networks, graph neural network equipment, devices, and media. Background technique [0002] Graph neural network is a new type of artificial intelligence neural network. Its input is graph structure data, and its output is representation vector, which is used to represent a high-level summary of properties and characteristics. The graph structure data includes representation vectors and edges of nodes. The representation vector of , the edge represents the association relationship between the connected nodes. Through training, the graph neural network can achieve far better results than traditional methods in tasks such as property prediction, classification, and regression. However, the traditional neural network training method does not consider the influence of edge representation vectors on the training results, and ignores...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/04
CPCG06N3/082G06N3/045G06N3/044
Inventor 陈鹏飞刘卫文谢昌谕陈广勇张胜誉
Owner TENCENT TECH (SHENZHEN) CO LTD
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