Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective

Author:

Liu Yunfei1ORCID,Li Jintang1ORCID,Chen Yuehe2ORCID,Wu Ruofan2ORCID,Wang Ericbk2ORCID,Zhou Jing2ORCID,Tian Sheng2ORCID,Shen Shuheng2ORCID,Fu Xing2ORCID,Meng Changhua2ORCID,Wang Weiqiang2ORCID,Chen Liang3ORCID

Affiliation:

1. Ant Group, Hangzhou, Zhejiang, China

2. Ant Group, Hangzhou, China

3. Unaffiliated, Guangzhou, China

Publisher

ACM

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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