A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

被引：32

作者：

Chen, Jianrui ^{[1
]}

Wang, Hua ^{[1
]}

Wang, Lina ^{[1
]}

Liu, Weiwei ^{[1
]}

机构：

[1] Inner Mongolia Univ Technol, Coll Sci, Hohhot 010051, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2016年 / 447卷

基金：

中国国家自然科学基金;

关键词：

Signed networks; Similarity; Neighbor; Dynamic evolution; Consensus;

D O I：

10.1016/j.physa.2015.12.006

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：482 / 492

页数：11

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