Node-graph cross-layer graph matching method and system based on graph neural network

A neural network and convolutional neural network technology, applied in the field of "node-graph" cross-layer graph matching, can solve the problems of not being able to perceive finer-grained interaction information, not considering interaction information at the same time, and not considering interaction information, etc.

Pending Publication Date: 2020-08-04
ZHEJIANG UNIV
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Problems solved by technology

Although the graph vectors of the two graph data contain the important information of each graph in this calculation method, it is impossible to perceive the finer-grained interaction information between the embedding vectors at different levels of the two graphs.
[0006] In general, the current methods of using neural networks to calculate the similarity of graph dat

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  • Node-graph cross-layer graph matching method and system based on graph neural network
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  • Node-graph cross-layer graph matching method and system based on graph neural network

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[0052] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention, but do not limit it in any way.

[0053] Such as figure 1 As shown, the graph similarity calculation system based on cross-layer graph matching network includes four modules: graph node embedding vector encoding module, "node-graph" cross-layer graph matching network module, graph vector encoding module and similarity calculation module.

[0054] The role of the graph node embedding vector encoding module is to initialize the feature matrix and adjacency matrix of the two graphs, and learn the feature vector of each node in the graph. Its workflow is as follows figure 2 As shown, the specific steps are as follows:

[0055] (1) Given a set of graph data (G 1 , G 2 ), using the feature information of the nodes and edges in th...

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Abstract

The invention discloses a node-graph cross-layer graph matching system based on a graph neural network. The system comprises a graph node embedding vector encoding module, a node-graph cross-layer graph matching network module, a graph vector encoding module and a similarity calculation module. In addition, the invention further discloses a node-graph cross-layer graph matching method based on thegraph neural network. The method is based on a graph neural network. Learning cross-level interaction information of each node in one graph and the whole other graph in a fine-grained manner; according to the method, the embedded information of each node can be effectively updated, and a cross-layer graph matching system based on the method can be obtained by training based on an end-to-end mode,so that the similarity between any two graph data can be quickly and accurately calculated.

Description

technical field [0001] The invention relates to the field of graph data mining, in particular to a "node-graph" cross-layer graph matching method and system based on a graph neural network. Background technique [0002] With the continuous development of the big data era, graph data not only shows an exponential growth in quantity, but also is ubiquitous in the current big data environment. Graph data has a wide range of application scenarios, such as bioinformatics, chemical drugs, recommendation systems, social networks, static program analysis and other fields. [0003] The calculation of similarity between graph data is a basic problem in the application process of graph data, that is, given a specific graph data, it is necessary to search for one or several similar graph data from a database containing a large number of graph data. Therefore, in order to measure the similarity between graph data, two common metrics are currently proposed: graph edit distance and maximu...

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Application Information

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IPC IPC(8): G06F16/903G06F16/901G06N3/04G06N3/08
CPCG06F16/90335G06F16/9024G06N3/08G06N3/045
Inventor 纪守领凌祥王赛卓陈建海吴春明
Owner ZHEJIANG UNIV
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