https://doi.org/10.1140/epjb/e2012-30697-5
Regular Article
EAMCD: an efficient algorithm based on minimum coupling distance for community identification in complex networks
1
School of Electronics and Information, Tongji
University, Shanghai
201804, PR
China
2
School of Mathematics and Physics, Shanghai Dian Ji
University, Shanghai
201306, PR
China
a e-mail: yanwu@tongji.edu.cn
Received:
28
July
2012
Received in final form:
28
July
2012
Published online:
21
January
2013
Community structure is an important feature in many real-world networks, which can help us understand structure and function in complex networks better. In recent years, there have been many algorithms proposed to detect community structure in complex networks. In this paper, we try to detect potential community beams whose link strengths are greater than surrounding links and propose the minimum coupling distance (MCD) between community beams. Based on MCD, we put forward an optimization heuristic algorithm (EAMCD) for modularity density function to welded these community beams into community frames which are seen as a core part of community. Using the principle of random walk, we regard the remaining nodes into the community frame to form a community. At last, we merge several small community frame fragments using local greedy strategy for the modularity density general function. Real-world and synthetic networks are used to demonstrate the effectiveness of our algorithm in detecting communities in complex networks.
Key words: Statistical and Nonlinear Physics
© EDP Sciences, Società Italiana di Fisica and Springer-Verlag, 2013