Adaptive clustering algorithm for community detection in complex networks.
Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality.
Ye
Zhenqing
Z
James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China.
Hu
Songnian
S
Yu
Jun
J
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019058:Community Networks; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results