| Title | Community Discovery with Location-Interaction Disparity in Mobile Social Networks |
| Authors | Danmeng Liu Wei Wei Guojie Song Ping Lu |
| Affiliation | Peking University, Beijing 100871, China ZTE Corporation, Shenzhen 518057, China |
| Keywords | mobile social network community detection LID mobile social network community detection LID |
| Issue Date | 2015 |
| Publisher | 中兴通讯技术（英文版） |
| Citation | 中兴通讯技术（英文版）.2015,(2),53-61. |
| Abstract | With the fast?growth of mobile social network, people ’s inter?actions 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 demo?graphics. However, there is little research on community dis?covery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one sim?ple 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 commu?nity detection results. We also propose a hybrid community?detection algorithm using LID for discovering location?based communities effectively and efficiently. Experiments on syn?thesis networks show that this algorithm can run effectively in time and discover communities with high precision. In real?world networks, the method reveals people ’s different social circles in different places with high efficiency. With the fast?growth of mobile social network, people ’s inter?actions 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 demo?graphics. However, there is little research on community dis?covery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one sim?ple 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 commu?nity detection results. We also propose a hybrid community?detection algorithm using LID for discovering location?based communities effectively and efficiently. Experiments on syn?thesis networks show that this algorithm can run effectively in time and discover communities with high precision. In real?world networks, the method reveals people ’s different social circles in different places with high efficiency. |
| URI | http://hdl.handle.net/20.500.11897/446404 |
| ISSN | 1673-5188 |
| Appears in Collections: | 待认领 |
