arrow
Return

Constrained Fuzzy Community Detection by a New Modularity Optimization Framework

delete2024-09-01
delete0
PRE
AI
J
Jing Xiao
Y
Yifan Guo
Y
Yu-Qing He
许
许小可 (Xiao-Ke Xu) *
DOI:10.1109/TNSE.2024.3420911delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Fuzzy community detection can not only reveal crisp community structures of real-life networks, but also provide fine-grained information of node fuzzy membership. However, existing methods for detecting fuzzy partitions often produce inaccurate fuzzy memberships for bridge nodes that do not align with the prior ground-truth crisp/hard partition, which diminishes the reliability and usefulness of fine-grained community information. To solve this issue, we propose a concept of constrained fuzzy partition and the corresponding constrained fuzzy community detection problem. This problem is formulated as a constrained fuzzy modularity maximization task to approximate the modularity-optimal constrained fuzzy partition, in which the fuzzy memberships can accurately quantify node membership to ground-truth communities and association strength between communities. To improve modularity quality and computational efficiency, a general framework of Constrained Fuzzy Modularity Maximization (CFMM) is proposed, which includes two key components: Fuzzy Membership Constraint (FMC) strategy and Node Dimension Reduction (NDR) operation. FMC constrains fuzzy membership of each node to be consistent with its ground-truth hard community and improves the quality of fuzzy partitions, and NDR reduces the time complexity of modularity evaluation by removing converged fuzzy membership of non-bridge nodes. Experimental results on synthetic and real-life networks prove the effectiveness of CFMM on state-of-the-art algorithms in terms of accuracy, quality, and efficiency, providing more precise and high-quality fuzzy community information.
Keywords:
Optimization
Bridges
Accuracy
Biology
Time complexity
Task analysis
Social networking (online)
Fuzzy community detection
fuzzy modularity maximization
constrained optimization
evolutionary algorithm

Journal

I
IEEE Transactions on Network Science and Engineering
IF:
7.9
Papers:
2.6K
Citations:
10.0K

Organization

B
Beijing Normal University
Scholars:
3.3W
Papers: 2.7W
Citations: 4.2W
D
Dalian Minzu University
Scholars:
2.0K
Papers: 1.7K
Citations: 2.6K
Cited Papers

Cited Papers

err
IF0
err
err0
PREAI
err
errShare
errSave
Community-based link prediction
err2017-01-11
err39
PREAI
errBiswas, Anupam; Biswas, Bhaskar
errShare
errSave
err
IF0
err
err0
PREAI
err
errShare
errSave
A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
err2013-12-01
err48
PREAI
errHavens, Timothy C.; Bezdek, James C.; Leckie, Christopher; Ramamohanarao, Kotagiri; Palaniswami, Marimuthu
errShare
errSave
researcher View more