相似性(几何)
不相交集
相似性度量
模糊逻辑
数据挖掘
聚类系数
数学
计算机科学
公制(单位)
聚类分析
节点(物理)
度量(数据仓库)
图形
模糊聚类
集合(抽象数据类型)
模糊集
人工智能
理论计算机科学
离散数学
图像(数学)
结构工程
工程类
经济
程序设计语言
运营管理
作者
Uttam K. Roy,Pranab K. Muhuri,Sajib K. Biswas
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:52 (10): 10014-10026
被引量:7
标识
DOI:10.1109/tcyb.2021.3071542
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(n2).
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