A fast algorithm for finding community structure based on community closeness
Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a /ast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk + mt), where k denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding.
community structure complex network data mining community closeness
Xiufang Jiang Guiquan Liu Zhiting Lin
Key Laboratory of Software in Computing and Communication, Anhui Province School of Computer Science and Technology University of Science and Technology of China, Hefei, Anhui 230027, China
国际会议
黄山
英文
436-439
2010-05-28（万方平台首次上网日期，不代表论文的发表时间）