A framework of graph classification with self-supervised heterogeneous graph neural network

doi:10.7523/j.ucas.2023.048

Abstract

Abstract: Graph data exists widely in various forms, and the tasks of graph classification have great significance for many problems. However, the tasks of graph classification still face many challenges, including how to make full use of the semantic information contained in the graph structures, and how to further reduce the computational complexity and the cost of obtaining labels. In this paper, a construction method of a hyper-node heterogeneous network is proposed for the first time, along with a new framework, GChgnn, which can be applied to graph classification. The GChgnn framework achieves the following goals through the introduction of a double-view graph representation mechanism and self-supervised contrastive learning: 1)measuring the similarity between the objectives of the large-scale graph classification tasks; 2)inspired by the graph matching methods, improving the accuracy of similarity measurement by the cross-graph idea, and making up for the lack of the explicit expression of graph embedding; 3)avoid the need to design complicated convolution and pooling operators in the network. After testing on some public datasets, the framework outperforms other existing methods.

Key words: graph classification, heterogeneous graph neural network, self-supervised learning, contrastive learning

CLC Number:

TP389.1

YUAN Ming, ZHAO Tong. A framework of graph classification with self-supervised heterogeneous graph neural network[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(6): 830-841.

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