Sakineh “Audrey” Yazdanparast (ECE), Timothy C. Havens (CC), and Mohsen Jamalabdollahi have authored “Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization,” which is available under the “Early Access” area on IEEE Xplore.
Extract
Soft overlapping clustering is one of the notable problems of community detection. Extensive research has been conducted to develop efficient methods for non-overlapping and crisp-overlapping community detection in large-scale networks. In this paper, Fast Fuzzy Modularity Maximization (FFMM) for soft overlapping community detection is proposed. FFMM exploits novel iterative equations to calculate the modularity gain associated with changing the fuzzy membership values of network vertices. The simplicity of the proposed scheme enables efficient modifications, reducing computational complexity to a linear function of the network size and the number of communities.
Citation
S. Yazdanparast, T. C. Havens and M. Jamalabdollahi, “Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization,” in IEEE Transactions on Fuzzy Systems.
DOI: 10.1109/TFUZZ.2020.2980502