Abstract
Recently, multi-view attributed graph clustering has attracted lots of attention with the explosion of graph-structured data. Existing methods are primarily designed for the form in which every graph has its attributes. We argue that a more natural form of multi-view attributed graph data contains shared node attributes and multiple graphs, which we called “multi-graph”. When simply applying existing methods to multi-graph clustering, the information of shared attributes is not well exploited to eliminate the large variances among different graphs. Therefore, we propose a Shared-Attribute Multi-Graph Clustering with global self-attention (SAMGC) method for multi-graph clustering. The main ideas of SAMGC are: 1) Global self-attention is proposed to construct the supplementary graph from shared attributes for each graph. 2) Layer attention is proposed to meet the requirements for different layers in different graphs. 3) A novel self-supervised weighting strategy is proposed to de-emphasize unimportant graphs. Our experiments on four benchmark datasets show the superiority of SAMGC over 14 SOTA methods. The source code is available at https://github.com/cjpcool/SAMGC.
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Acknowledgements
This work was supported in part by Sichuan Science and Technology Program (Nos. 2021YFS0172, 2022YFS0047, and 2022YFS0055), Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China (No. ZYGX2021YGLH022), Guangzhou Science and Technology Program (No. 202002030266), Opening Funds from Radiation Oncology Key Laboratory of Sichuan Province (No. 2021ROKF02), and Major Science and Technology Application Demonstration Project of Chengdu Science and Technology Bureau (No. 2019-YF09-00086-SN).
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Chen, J. et al. (2023). Shared-Attribute Multi-Graph Clustering with Global Self-Attention. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_5
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