TY - JOUR
T1 - A label-based evolutionary computing approach to dynamic community detection
AU - Niu, Xinzheng
AU - Si, Weiyu
AU - Wu, Chase Q.
N1 - Funding Information:
This work was supported by the Soft Science Research Project of Chengdu Science and Technology Bureau (2015-RK00-00247-ZF), the Scientific Research Project of Sichuan Provincial Public Security Department (2015SCYYCX06), and the National Natural Science Foundation of China (61300192).
Publisher Copyright:
© 2017
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Dynamic community detection is the process to discover the structure of and determine the number of communities in dynamic networks consisting of a series of temporal network snapshots. Due to the time-varying characteristics of such networks, community detection must consider both the quality of the community structure and the temporal cost that quantifies the difference between the current network snapshot and previous ones. In this paper, we propose a label-based multi-objective optimization algorithm for dynamic community detection, which employs a genetic algorithm to optimize two objectives, i.e. clustering quality and temporal cost. A label propagation method is designed and used to initialize the network's communities and restrict the conditions of the mutation process to further improve the detection efficiency and effectiveness. We conduct experiments on both synthesized and empirical datasets, and extensive results illustrate that the proposed method outperforms a state-of-the-art algorithm in terms of detection quality and speed, which sheds light on its wide applications to various complex networks with dynamic structures such as rapidly growing online social networks.
AB - Dynamic community detection is the process to discover the structure of and determine the number of communities in dynamic networks consisting of a series of temporal network snapshots. Due to the time-varying characteristics of such networks, community detection must consider both the quality of the community structure and the temporal cost that quantifies the difference between the current network snapshot and previous ones. In this paper, we propose a label-based multi-objective optimization algorithm for dynamic community detection, which employs a genetic algorithm to optimize two objectives, i.e. clustering quality and temporal cost. A label propagation method is designed and used to initialize the network's communities and restrict the conditions of the mutation process to further improve the detection efficiency and effectiveness. We conduct experiments on both synthesized and empirical datasets, and extensive results illustrate that the proposed method outperforms a state-of-the-art algorithm in terms of detection quality and speed, which sheds light on its wide applications to various complex networks with dynamic structures such as rapidly growing online social networks.
KW - Community detection
KW - Dynamic networks
KW - Evolutionary clustering
KW - Label propagation
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U2 - 10.1016/j.comcom.2017.04.009
DO - 10.1016/j.comcom.2017.04.009
M3 - Article
AN - SCOPUS:85020005186
SN - 0140-3664
VL - 108
SP - 110
EP - 122
JO - Computer Communications
JF - Computer Communications
ER -