PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering-Reference-Cited by-同舟云学术

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PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering

Published:2024-08-24 Issue: Volume:353 Page:1793-1803
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Lin Longlong¹^ORCID,Jia Tao¹^ORCID,Wang Zeli²^ORCID,Zhao Jin³^ORCID,Li Rong-Hua⁴^ORCID

Affiliation:

1. College of Computer and Information Science, Southwest University, Chongqing, China

2. Chongqing University of Post and Telecommunications, Chongqing, China

3. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

4. Shenzhen Institute of Technology & Beijing Institute of Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671666

Reference74 articles.

1. λ1, Isoperimetric inequalities for graphs, and superconcentrators

2. Motif-based communities in complex networks

3. Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512.

4. Austin R Benson, David F Gleich, and Jure Leskovec. 2016. Higher-order organization of complex networks. Science, Vol. 353, 6295 (2016), 163--166.

5. Set Partitioning via Inclusion-Exclusion

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