A Community Clustering Algorithm Based on Genetic Algorithm With Novel Coding Scheme

被引：0

作者：

Li, Xianghua ^{[1
]}

Gao, Chao ^{[1
,2
]}

Pu, Ruyang ^{[1
]}

机构：

[1] Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China

[2] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun, Peoples R China

来源：

2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2014年

基金：

美国国家科学基金会;

关键词：

Complex networks; community detection; genetic algorithm; attribute partition;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Community structure is one of the basic characteristics of a complex network, which plays an important role in the function of a network. According to the premature convergence of traditional genetic algorithm on community detection, this paper proposes a new coding scheme based on the attribute partition of edges. The new strategy is named as NGACD. Each nonzero gene in the NGACD represents the attribute partition between two nodes. Based on the novel coding scheme, NGACD is feasible for crossover and mutation operations. Specifically, the NGACD is independent of the context and exhibits the more features of modularity. Four benchmark network are used to estimate the efficiency of proposed strategy. The simulation results show that our algorithm is more accurate and stable than others.

引用

页码：486 / 491

页数：6

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