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Contrastive Disentangled Graph Convolutional Network for Weakly-Supervised Classification

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13245)


Node classification on graph-structured data plays an important role in many machine learning applications. Recently, Graph Convolutional Networks (GCNs) have shown remarkable success in the node classification task, due to the ability to aggregate neighborhood information and propagate supervised signals over the graph. However, most GCN-style models require relatively sufficient labeled data, which are not available in many real-world applications. Therefore, we in this paper study the problem of weakly-supervised node classification and propose a Contrastive Disentangled Graph Convolutional Network (CDGCN) to learn disentangled node representations based on the contrastive learning mechanism. Extensive experimental results show that CDGCN significantly outperforms all baselines on different label sparsities. The code is available at


  • Graph Convolutional Network
  • Disentangled representation
  • Contrastive learning
  • Weakly-supervised node classification

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  • DOI: 10.1007/978-3-031-00123-9_57
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This work has been supported by the National Natural Science Foundation of China under Grant No.: 62077044, 61702470, 62002343.

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Correspondence to Jingping Bi .

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Chu, X. et al. (2022). Contrastive Disentangled Graph Convolutional Network for Weakly-Supervised Classification. In: , et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00122-2

  • Online ISBN: 978-3-031-00123-9

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