Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Author:
Affiliation:
1. Ant Group, Hangzhou, Zhejiang, China
2. Ant Group, Hangzhou, China
3. Unaffiliated, Guangzhou, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671967
Reference51 articles.
1. DFuzzy: a deep learning-based fuzzy clustering model for large graphs
2. Aritra Bhowmick, Mert Kosan, Zexi Huang, Ambuj K. Singh, and Sourav Medya. 2024. DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization. In AAAI. AAAI Press, 11069--11077.
3. Fast unfolding of communities in large networks
4. Ulrik Brandes, Daniel Delling, Marco Gaertler, Rachelle Goerke, Martin Hoefer, Zoran Nikoloski, and Donald Wagner. 2006. Maximizing Modularity is hard. arXiv: Data Analysis, Statistics and Probability (2006).
5. Networks—An Introduction. Mark E. J. Newman. (2010, Oxford University Press.) $65.38, £35.96 (hardcover), 772 pages. ISBN-978-0-19-920665-0.
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