Real-world social relationship estimation for mobile social network

摘要：

　　The growing ubiquity of mobile devices allows users to form mobile social networks (MSN) which create new means for virtual interactions on the basis of their users’ physical features.Many MSNs can benefit from the knowledge of their users’ real-world social relationships.In this paper,we propose an approach for estimating users’ real-world interpersonal social relationships by leveraging two kinds of physical features,i.e.location and proximity which are respectively represented by GPS locations and Bluetooth detections in mobile environment.To estimate the real-world social relationships between a user and all his encounters,the approach firstly extracts places with semantic meanings (e.g.home,work,etc.) where the user has visited,and then the social relationships are estimated by integrating both the extracted places and the proximity data.The experimental results show that our approach can achieve an accuracy of over 90% in estimating three types of real-world social relationships (i.e.family,colleague and friend).

关键词： mobile social network social relationship spatial data mining

作者: Mingqi Lv Ling Chen Gencai Chen

作者单位: College of Computer Science, Zhejiang University, Hangzhou 310027, China;Hangzhou Normal University, College of Computer Science, Zhejiang University, Hangzhou 310027, China

会议类型: 国内会议

会议名称: 第21届全国多媒体技术、第8届全国普适计算、第8届全国人机交互联合学术会议

会议地点: 广州

会议语种:英文

页码: 1-8

在线出版日期: 2012-11-23（万方平台首次上网日期，不代表论文的发表时间）