Paper Info
Title
A cellular learning automata based algorithm for detecting community structure in complex networks.
Abstract
Community structure is a common and important property of complex networks. The detection of communities has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a modularity optimization problem. However, traditional modularity optimization based algorithms have the resolution limit that they may fail to find communities which are smaller than a certain size. In this paper, we propose a cellular learning automata based algorithm for detecting communities in complex networks. Our algorithm models the whole network as an irregular cellular learning automata (ICLA) and reveals the optimal community structure through the evolution of the cellular learning automata. By interacting with both the global and local environments, our algorithm effectively solves the resolution limit problem of modularity optimization. The experiments on both synthetic and real-world networks demonstrate that our algorithm is effective and efficient at detecting community structure in complex networks.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.04.087
Neurocomputing
Keywords
Field
DocType
Community detection,Complex network,Modularity optimization,Resolution limit,Cellular learning automata
Modularity (networks),Community structure,Computer science,Algorithm,Theoretical computer science,Cellular learning automata,Complex network,Artificial intelligence,Clique percolation method,Optimization problem,Machine learning,Modularity
Journal
Volume
ISSN
Citations 
151
0925-2312
11
PageRank 
References 
Authors
0.50
22
6
Name
Order
Citations
PageRank
Yuxin Zhao125535.38
W.-G. Jiang2263.43
Shenghong Li335747.31
Yinghua Ma4377.12
Guiyang Su5121.54
Xiang Lin6110.50