Community detection in signed networks based on discrete-time model

doi:10.1088/1674-1056/26/1/018901

中国物理B ›› 2017, Vol. 26 ›› Issue (1): 18901-018901.doi: 10.1088/1674-1056/26/1/018901

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

Community detection in signed networks based on discrete-time model

Jianrui Chen(陈建芮), Li Zhang(张莉), Weiwei Liu(刘维维), Zaizai Yan(闫在在)

College of Science, Inner Mongolia University of Technology, Hohhot 010051, China

收稿日期:2016-05-19 修回日期:2016-06-26 出版日期:2017-01-05 发布日期:2017-01-05
通讯作者: Jianrui Chen E-mail:jianrui_chen@sina.com
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 11261034, 71561020, 61503203, and 11326239), the Higher School Science and Technology Research Project of Inner Mongolia, China (Grant No. NJZY13119), and the Natural Science Foundation of Inner Mongolia, China (Grant Nos. 2015MS0103 and 2014BS0105).

Community detection in signed networks based on discrete-time model

Jianrui Chen(陈建芮), Li Zhang(张莉), Weiwei Liu(刘维维), Zaizai Yan(闫在在)

College of Science, Inner Mongolia University of Technology, Hohhot 010051, China

Received:2016-05-19 Revised:2016-06-26 Online:2017-01-05 Published:2017-01-05
Contact: Jianrui Chen E-mail:jianrui_chen@sina.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 11261034, 71561020, 61503203, and 11326239), the Higher School Science and Technology Research Project of Inner Mongolia, China (Grant No. NJZY13119), and the Natural Science Foundation of Inner Mongolia, China (Grant Nos. 2015MS0103 and 2014BS0105).

摘要/Abstract

摘要： Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other. Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.

关键词: community detection, signed networks, discrete-time model, similarity

Abstract: Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other. Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.

Key words: community detection, signed networks, discrete-time model, similarity

中图分类号: (Structures and organization in complex systems)

89.75.Fb

89.75.Hc (Networks and genealogical trees) 84.30.Bv (Circuit theory)

陈建芮, 张莉, 刘维维, 闫在在. Community detection in signed networks based on discrete-time model[J]. 中国物理B, 2017, 26(1): 18901-018901.

Jianrui Chen(陈建芮), Li Zhang(张莉), Weiwei Liu(刘维维), Zaizai Yan(闫在在). Community detection in signed networks based on discrete-time model[J]. Chin. Phys. B, 2017, 26(1): 18901-018901.

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Community detection in signed networks based on discrete-time model

Community detection in signed networks based on discrete-time model

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