ELU-GCN: Effectively Label-Utilizing Graph Convolutional Network

• URL: http://arxiv.org/abs/2411.02279v1
• Date: Mon, 04 Nov 2024 17:08:59 GMT
• Title: ELU-GCN: Effectively Label-Utilizing Graph Convolutional Network
• Authors: Jincheng Huang, Yujie Mo, Xiaoshuang Shi, Lei Feng, Xiaofeng Zhu,
• Abstract summary: 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. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning.
• Score: 17.273475235903355
• License:
• 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 (\ie 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.

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