Deep graph clustering network by community prediction-guided augmentation

Zheyu Zheng; Yizhe Shang; Congcong Sun; Jianrui Chen

doi:10.1117/12.3035234

11 July 2024 Deep graph clustering network by community prediction-guided augmentation

Zheyu Zheng, Yizhe Shang, Congcong Sun, Jianrui Chen

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100Q (2024) https://doi.org/10.1117/12.3035234
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Deep graph clustering is a challenging field that help us analyze complex network data which involves dividing nodes into non-overlapping clusters. A key issue in this field is the reliance on complex data augmentation. Traditional data augmentation methods not only increase data processing time, but also require specialized knowledge to determine appropriate strategies. At the same time, traditional data augmentation may not fully utilize the topological properties of graph data, thus limiting the potential of graph clustering models. Based on these, we propose a novel unsupervised learning approach for automatically identifying and implementing effective data augmentation strategies, focusing on community-based augmentation. This approach dynamically analyzes graph data structures, reduces manual intervention, and improves the efficiency and accuracy of deep graph clustering. Our experimental results validate the efficiency of our method, which outperforms the models without considering information enhancement.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheyu Zheng, Yizhe Shang, Congcong Sun, and Jianrui Chen "Deep graph clustering network by community prediction-guided augmentation", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100Q (11 July 2024); https://doi.org/10.1117/12.3035234

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