NeSiFC: Neighbors' Similarity-Based Fuzzy Community Detection Using Modified Local Random Walk

作     者：Roy, Uttam K. Muhuri, Pranab K. Biswas, Sajib K. 

作者机构：South Asian Univ Dept Comp Sci New Delhi 110021 India 

出 版 物：《IEEE TRANSACTIONS ON CYBERNETICS》 (IEEE Trans. Cybern.)

年 卷 期：2022年第52卷第10期

页      面：10014-10026页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：South Asian University 

主　　题：Detection algorithms Social networking (online) Measurement Tuning Indexes Benchmark testing Complex networks Community detection complex social network analysis fuzzy community detection (FCD) modified local random walk (mLRW) overlapping communities peripheral similarity index (PSI) quality and accuracy metrics 

摘      要：This article proposes a neighbors similarity-based fuzzy community detection (FCD) method, which we call ``NeSiFC. In the proposed NeSiFC approach, we compute the similarity between two neighbors by introducing a modified local random walk (mLRW). Basically, in a network, a node and its neighbors with noticeable similarities among them construct a community. To measure this similarity, we introduce a new metric, called the peripheral similarity index (PSI). This PSI is used to construct the transition probability matrix for the mLRW. The mLRW is applied for each node until it meets a parameter called step coefficient. The mLRW gives better neighbors similarity for community detection. Finally, a fuzzy membership function is used iteratively to compute the membership degrees for all nodes with reference to existing communities. The proposed NeSiFC has no dependence on the network characteristics, and no adjustment or fine tuning of more than one parameter is needed. To show the efficacy of the proposed NeSiFC approach, we provide a thorough comparative performance analysis considering a set of well-known FCD algorithms viz., the genetic algorithm for fuzzy community detection, membership degree propagation, center-based fuzzy graph clustering, FMM/H2, and FuzAg on a set of popular benchmarks, as well as real-world datasets. For both disjoint and overlapping community structures, results of various accuracy and quality metrics indicate the outstanding performance of our proposed NeSiFC approach. The asymptotic complexity of the proposed NeSiFC is found as O(n(2)).