Exploiting Social and Mobility Patterns for Friendship Prediction in Location-Based Social Networks

被引:0
|
作者
Valverde-Rebaza, Jorge [1 ]
Roche, Mathieu [2 ]
Poncelet, Pascal [3 ]
Lopes, Alneu de Andrade [1 ]
机构
[1] Univ Sao Paulo, ICMC, Sao Paulo, Brazil
[2] Cirad, TETIS & LIRMM, Montpellier, France
[3] Univ Montpellier, LIRMM, Montpellier, France
基金
巴西圣保罗研究基金会;
关键词
Link prediction; Location-based social networks; Friendship prediction; Mobility patterns; User behavior;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Link prediction is a "hot topic" in network analysis and has been largely used for friendship recommendation in social networks. With the increased use of location-based services, it is possible to improve the accuracy of link prediction methods by using the mobility of users. The majority of the link prediction methods focus on the importance of location for their visitors, disregarding the strength of relationships existing between these visitors. We, therefore, propose three new methods for friendship prediction by combining, efficiently, social and mobility patterns of users in location-based social networks (LBSNs). Experiments conducted on real-world datasets demonstrate that our proposals achieve a competitive performance with methods from the literature and, in most of the cases, outperform them. Moreover, our proposals use less computational resources by reducing considerably the number of irrelevant predictions, making the link prediction task more efficient and applicable for real world applications.
引用
收藏
页码:2526 / 2531
页数:6
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