Community detection using global and local structural information

Pramana

Fang Chane¹  Xin-Guang Hu³  Ju Xiang2¹  Xiao-Yu Zhang¹  Er-Min Guo¹  Jun-Feng Fan¹  Ru-Min Wang²  Ke Hu⁴  Gen-Yi Fu¹  Hai-Long Yan² 

[1] Department of Basic Sciences, The First Aeronautical Institute of the Air Force, Xinyang 464000, Henan, China$$;College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang 464000, Henan, China$$;School of Information Engineering, HuangShan University, HuangShan 245021, Anhui, China$$;Department of Physics, Xiangtan University, Xiangtan 411105, Hunan, China$$

关键词: Complex network;    community structure;    edge betweenness;    local random walk.;   

DOI  :  

学科分类：物理（综合）

来源: Indian Academy of Sciences

【 摘 要 】

Community detection is of considerable importance for understanding both the structure and function of complex networks. In this paper, we introduced the general procedure of the community detection algorithms using global and local structural information, where the edge betweenness and the local similarity measures respectively based on local random walk dynamics and local cyclic structures were used. The algorithms were tested on artificial and real-world networks. The results clearly show that all the algorithms have excellent performance in the tests and the local similarity measure based on local random walk dynamics is superior to that based on local cyclic structures.

【 授权许可】

Unknown