[an error occurred while processing this directive]

ZTE Communications ›› 2015, Vol. 13 ›› Issue (2): 53-61.DOI: 10.3969/j.issn.1673-5188.2015.02.010

• • 上一篇    

Community Discovery with Location-Interaction Disparity in Mobile Social Networks

Danmeng Liu1, Wei Wei2, Guojie Song1, Ping Lu2   

  1. 1. Peking University, Beijing 100871, China;
    2. ZTE Corporation, Shenzhen 518057, China
  • 收稿日期:2015-04-21 出版日期:2015-06-25 发布日期:2015-06-25
  • 作者简介:Danmeng Liu (liudanmeng@pku.edu.cn) received his Bachelor degree from Wuhan University. He is a Master candidate at school of School of Electronic Engineering and Computing Science, Peking University. His research interests include data mining and social network analysis.
    Wei Wei (wei.wei118@zte.com.cn) received her MS degree in communication and information engineering from Chongqing University of Posts and Telecommunications. Her research interests include location technology and business intelligence.
    Guojie Song (gjsong@pku.edu.cn) is an associate professor of the School of Electronic Engineering and Computing Science, and vice director of Research Center of Intelligent Information Processing, at Peking University. He received the PhD degree from Department of Computer Science, Peking University in 2004. He is currently interested in various techniques of data mining, machine learning, as well as their applications in intelligent traffic system, and social networks etc.
    Ping Lu (lu.ping@zte.com.cn) received his ME degree in automatic control theory and applications from South East University. He is the chief executive of the Service Institute of ZTE Corporation. His research interests include augmented reality and multimedia services technologies.
  • 基金资助:
    This work is supported by the National High Technology Research and Development Program of China under Grant No. 2014AA015103, Beijing Natural Science Foundation under Grant No. 4152023, the National Natural Science Foundation of China under Grant No. 61473006, and the National Science and Technology Support Plan under Grant No. 2014BAG01B02.

Community Discovery with Location-Interaction Disparity in Mobile Social Networks

Danmeng Liu1, Wei Wei2, Guojie Song1, Ping Lu2   

  1. 1. Peking University, Beijing 100871, China;
    2. ZTE Corporation, Shenzhen 518057, China
  • Received:2015-04-21 Online:2015-06-25 Published:2015-06-25
  • About author:Danmeng Liu (liudanmeng@pku.edu.cn) received his Bachelor degree from Wuhan University. He is a Master candidate at school of School of Electronic Engineering and Computing Science, Peking University. His research interests include data mining and social network analysis.
    Wei Wei (wei.wei118@zte.com.cn) received her MS degree in communication and information engineering from Chongqing University of Posts and Telecommunications. Her research interests include location technology and business intelligence.
    Guojie Song (gjsong@pku.edu.cn) is an associate professor of the School of Electronic Engineering and Computing Science, and vice director of Research Center of Intelligent Information Processing, at Peking University. He received the PhD degree from Department of Computer Science, Peking University in 2004. He is currently interested in various techniques of data mining, machine learning, as well as their applications in intelligent traffic system, and social networks etc.
    Ping Lu (lu.ping@zte.com.cn) received his ME degree in automatic control theory and applications from South East University. He is the chief executive of the Service Institute of ZTE Corporation. His research interests include augmented reality and multimedia services technologies.
  • Supported by:
    This work is supported by the National High Technology Research and Development Program of China under Grant No. 2014AA015103, Beijing Natural Science Foundation under Grant No. 4152023, the National Natural Science Foundation of China under Grant No. 61473006, and the National Science and Technology Support Plan under Grant No. 2014BAG01B02.

PDF

摘要： With the fast-growth of mobile social network, people’s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-Interaction Disparity (LID), we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid communitydetection algorithm using LID for discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people’s different social circles in different places with high efficiency.

关键词: mobile social network, community detection, LID

Abstract: With the fast-growth of mobile social network, people’s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-Interaction Disparity (LID), we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid communitydetection algorithm using LID for discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people’s different social circles in different places with high efficiency.

Key words: mobile social network, community detection, LID