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D2-GCN: a graph convolutional network with dynamic disentanglement for node classification

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Abstract

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which ignores the distinct impacts from different neighbors when aggregating their features to update a node’s representation. Disentangled GCNs have been proposed to divide each node’s representation into several feature units. However, current disentangling methods do not try to figure out how many inherent factors the model should assign to help extract the best representation of each node. This paper then proposes D2-GCN to provide dynamic disentanglement in GCNs and present the most appropriate factorization of each node’s mixed features. The convergence of the proposed method is proved both theoretically and experimentally. Experiments on real-world datasets show that D2-GCN outperforms the baseline models concerning node classification results in both single- and multi-label tasks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62141214 and 62272171).

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Authors and Affiliations

  1. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Shangwei Wu, Yingtong Xiong, Hui Liang & Chuliang Weng

Authors
  1. Shangwei Wu
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  2. Yingtong Xiong
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  3. Hui Liang
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  4. Chuliang Weng
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Correspondence to Chuliang Weng.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Shangwei Wu is currently a PhD candidate at East China Normal University (ECNU), China. His research interests include graph neural networks and deep learning systems.

Yingtong Xiong received the ME degree at East China Normal University (ECNU), China in 2023. Her research interests include graph neural networks and deep learning systems.

Hui Liang is pursuing the ME degree at East China Normal University (ECNU), China. His research interests include graph neural networks and deep learning systems.

Chuliang Weng received the PhD degree at Shanghai Jiao Tong University (SJTU), China in 2004. He is currently a full professor at East China Normal University (ECNU), China. Before joining ECNU, he was an associate professor at SJTU and later worked at Huawei Central Research Institute, China. He was also a visiting research scientist at Columbia University, USA. His research interests include parallel and distributed systems, system virtualization and cloud computing, storage systems, operating systems, and system security.

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Wu, S., Xiong, Y., Liang, H. et al. D2-GCN: a graph convolutional network with dynamic disentanglement for node classification. Front. Comput. Sci. 19, 191305 (2025). https://doi.org/10.1007/s11704-023-3339-7

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