• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Guo, K. (Guo, K..) [1] | Zhu, T. (Zhu, T..) [2] | Li, G.H. (Li, G.H..) [3]

Indexed by:

Scopus

Abstract:

Community detection is able to explore the individual sets with the same characteristics, which is helpful for people to understand the structures and functions of the networks more clearly. In this paper, an Incremental dynamic Community discovery algorithm based on Improved Modularity(ICIM) is proposed to find the dynamic network structures, the algorithm uses improved modularity as evaluation index of the communities. Community structures are adjusted according to the historical moment topology and the impact of incremental changes on the belonging coefficients of vertices neighbors with the change of the nodes and edges in the local area. Experimental results show that the algorithm can find community structure effectively and timely. © 2016 IEEE.

Keyword:

Dynamic community; Incremental clustering; Modularity; Social networks

Community:

  • [ 1 ] [Guo, K.]Dept. Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Zhu, T.]Dept. Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Li, G.H.]Dept. Mathematics and Computer Science, Fuzhou University, Fuzhou, China

Reprint 's Address:

Email:

Show more details

Related Keywords:

Related Article:

Source :

2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings

Year: 2017

Page: 2536-2541

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

Affiliated Colleges:

Online/Total:59/10027724
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1