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author:

Guo, Kun (Guo, Kun.) [1] (Scholars:郭昆) | Zhu, Tengyun (Zhu, Tengyun.) [2] | Li, Guo Hui (Li, Guo Hui.) [3]

Indexed by:

CPCI-S

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.

Keyword:

dynamic community incremental clustering modularity social networks

Community:

  • [ 1 ] [Guo, Kun]Fuzhou Univ, Dept Math & Comp Sci, Fuzhou, Fujian, Peoples R China
  • [ 2 ] [Zhu, Tengyun]Fuzhou Univ, Dept Math & Comp Sci, Fuzhou, Fujian, Peoples R China
  • [ 3 ] [Li, Guo Hui]Fuzhou Univ, Dept Math & Comp Sci, Fuzhou, Fujian, Peoples R China

Reprint 's Address:

  • 郭昆

    [Guo, Kun]Fuzhou Univ, Dept Math & Comp Sci, Fuzhou, Fujian, Peoples R China

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Source :

2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC)

Year: 2016

Page: 2536-2541

Language: English

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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