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SMGCL: Semi-supervised Multi-view Graph Contrastive Learning
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DOI：10.1016/j.knosys.2022.110120.png)
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Abstract
En 中文
Graph contrastive learning (GCL), aiming to generate supervision information by transforming the graph data itself, is increasingly becoming a focus of graph research. It has shown promising performance in graph representation learning by extracting global-level abstract features of graphs. Nonetheless, most GCL methods are performed in a completely unsupervised manner and would get unappealing results in balancing the multi-view information of graphs. To alleviate this, we propose a Semi-supervised Multi-view Graph Contrastive Learning (SMGCL) framework for graph classification. The framework can capture the comparative relations between label-independent and label-dependent node (or graph) pairs across different views. In particular, we devise a graph neural network (GNN)-based label augmentation module to exploit the label information and guarantee the discrimination of the learned representations. In addition, a shared decoder module is complemented to extract the un-derlying determinative relationship between learned representations and graph topology. Experimental results on graph classification tasks demonstrate the superiority of the proposed framework.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Graph Contrastive Learning
Graph neural network
Semi -supervised learning
Graph classification
Graph representation learning