A Memetic Algorithm for Community Detection in Bipartite Networks

被引：0

作者：

Wang, Xiaodong ^{[1
]}

Liu, Jing ^{[1
]}

机构：

[1] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV | 2017年 / 10637卷

基金：

中国国家自然科学基金;

关键词：

Community detection; Measures; Comparison; Bipartite networks;

D O I：

10.1007/978-3-319-70093-9_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Community detection is a basic tool to analyze complex networks. However, there are many community detection methods for unipartite networks while just a few methods for bipartite networks (BNs). In this paper, we propose a memetic algorithm (MACD-BNs) to identify communities in BNs. We use MACD-BNs to optimize two extended measures, namely Baber modularity (QB) and modularity density (QD), on real-life and synthetic networks respectively so as to compare their performance. We conclude that QD are more effective than QB when the size of communities is heterogeneous while QB is more suitable to detect communities with similar size. Besides, we also make a comparison between MACD-BNs and other community detection method and the results show the effectiveness of MACD-BNs.

引用

页码：89 / 99

页数：11

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