标题:Inferring Friendship from Check-in Data of Location-Based Social Networks
作者:Cheng, Ran; Pang, Jun; Zhang, Yang
作者机构:[Cheng, Ran; Pang, Jun] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust, Luxembourg, Luxembourg.; [Pang, Jun; Zhang, Yang] Univ Luxemb 更多
会议名称:IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
会议日期:AUG 25-28, 2015
来源:PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015)
出版年:2015
页码:1284-1291
DOI:10.1145/2808797.2808884
摘要:With the ubiquity of GPS-enabled devices and location-based social network services, research on human mobility becomes quantitatively achievable. Understanding it could lead to appealing applications such as city planning and epidemiology. In this paper, we focus on predicting whether two individuals are friends based on their mobility information. Intuitively, friends tend to visit similar places, thus the number of their co-occurrences should be a strong indicator of their friendship. Besides, the visiting time interval between two users also has an effect on friendship prediction. By exploiting machine learning techniques, we construct two friendship prediction models based on mobility information. The first model focuses on predicting friendship of two individuals with only one of their co-occurred places' information. The second model proposes a solution for predicting friendship of two individuals based on all their co-occurred places. Experimental results show that both of our models outperform the state-of-the-art solutions.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:3
Scopus被引频次:10
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962606510&doi=10.1145%2f2808797.2808884&partnerID=40&md5=4fb9eecec264bd3efaf4cda00e0f1775
TOP