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Graph similarity calculation method and device based on graph convolution network

A technology of convolutional network and calculation method, which is applied in the field of graph similarity calculation method and device based on graph convolutional network, can solve the problem that time and graph level cannot be taken into account, and achieve the effect of overcoming time-consuming and improving effectiveness.

Inactive Publication Date: 2021-03-19
SUN YAT SEN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a graph similarity calculation method and device based on graph convolutional network, which improves the effectiveness of graph similarity calculation by combining the flat and hierarchical representation of graphs in a reasonable time to solve the problem of The above-mentioned technical problems that cannot take into account both time and graph levels

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  • Graph similarity calculation method and device based on graph convolution network
  • Graph similarity calculation method and device based on graph convolution network
  • Graph similarity calculation method and device based on graph convolution network

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Embodiment Construction

[0048] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following description The embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0049] The embodiment of the present invention provides an end-to-end topological graph similarity calculation method capable of simultaneously learning graph flat and hierarchical information. While maintaining the same level of computational complexity as the latest method, the GscGCN (Graph Similarity Computation wi...

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Abstract

The invention discloses a graph similarity calculation method and device based on a graph convolution network, can overcome the defect that an existing GNN-based graph similarity calculation model cannot well learn the hierarchical structure of a graph, and improves the effectiveness of graph similarity calculation by combining the flatness and hierarchical representation of the graph together within reasonable time. Specifically, the embodiment of the invention provides a function which completely supports back propagation and is based on an end-to-end ground neural network, each part of thefunction is carefully designed, so that the function can learn flat and hierarchical information of graphs, and finally, a pair of graphs is mapped into similarity scores. The defects that in the prior art, consumed time is long, and the hierarchical structure of a graph cannot be captured are overcome.

Description

technical field [0001] The invention belongs to the technical field of graph similarity calculation, and in particular relates to a graph similarity calculation method and device based on a graph convolutional network. Background technique [0002] Topology is an abstract representation method that only uses nodes (Vertex) and edges (edge) to describe the relationship between multiple things without considering the physical properties such as the size and shape of things, and the model of the relationship between things represented by topology called a topology map. Topology does not care about the details of things, nor does it care about the mutual proportional relationship, but only in the form of a graph under certain circumstances. In the graph, nodes are used to represent things, and edges between nodes are used to represent the relationship between things, so as to abstract multiple interrelationships between things. Topological graphs can be used to abstract most i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/048G06N3/045G06F18/23G06F18/22
Inventor 刘玉葆李聪
Owner SUN YAT SEN UNIV
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