research-article

Mixed Graph Contrastive Network for Semi-supervised Node Classification

Authors:

En ZhuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 7

Article No.: 162, Pages 1 - 19

https://doi.org/10.1145/3641549

Published: 19 June 2024 Publication History

Abstract

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.

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Index Terms

Mixed Graph Contrastive Network for Semi-supervised Node Classification
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 7

August 2024

505 pages

EISSN:1556-472X

DOI:10.1145/3613689

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 19 June 2024

Online AM: 05 February 2024

Accepted: 08 January 2024

Revised: 12 December 2023

Received: 18 August 2023

Published in TKDD Volume 18, Issue 7

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Zhu LSun HHuang XLou PHe L(2025)Contrastive deep graph clustering with hard boundary sample awarenessInformation Processing & Management10.1016/j.ipm.2024.10405062:3(104050)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104050
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https://doi.org/10.1016/j.eswa.2025.126571
Wan XLiu JLiu XWen YYu HWang SYu SWan TWang JZhu ESalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Decouple then classifyProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694112(49941-49956)Online publication date: 21-Jul-2024
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Huang JShen JShi XZhu XSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)On which nodes does GCN fail? enhancing GCN from the node perspectiveProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692879(20073-20095)Online publication date: 21-Jul-2024
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