ELU-GCN: Effectively Label-Utilizing Graph Convolutional Network

Jincheng Huang, Yujie Mo, Lei Feng, Xiaoshuang Shi, Xiaofeng Zhu

Keywords: graph neural network, semi-supervised learning, node classification

Abstract: The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to be propagated to a broader range of neighbors, thereby increasing the utilization of labels. However, the label information is not always effectively utilized in the traditional GCN framework. To address this issue, we propose a new two-step framework called ELU-GCN. In the first stage, ELU-GCN conducts graph learning to learn a new graph structure (i.e., ELU-graph), which enables GCNs to effectively utilize label information. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning by exploring the consistency and mutually exclusive information between the learned ELU graph and the original graph. Moreover, we theoretically demonstrate that the proposed method can ensure the generalization ability of GCNs. Extensive experiments validate the superiority of the proposed method.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 3482