Exploiting social and mobility patterns for friendship prediction in location-based social networks

Abstract

Abstract-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.

The role of location and social strength for friendship prediction in location-based social networks

“…These platforms, also called as online social networks (OSNs), have become part of the daily life of millions of people around the world who constantly maintain and create new social relationships [2,3]. OSNs providing location-based services for users to check-in in a physical place are called location-based social networks (LBSNs) [4,5,6,7].…”

“…links connecting users, and location prediction when the focus is to predict userlocation links, i.e. links connecting users with places [6,14,15].…”

Mining user behavior in location-based social networks

“…This is defined as the proportion of correctly predicted links into the total number of truly new links (FATOURECHI et al, 2008;PHAM;VALVERDE-REBAZA et al, 2016), as stated in Eq. 2.31.…”

“…This is the proportion of correctly predicted links into the prediction space (FA-TOURECHI et al, 2008;PHAM;VALVERDE-REBAZA et al, 2016). This measure is computed as:…”

“…The scores represent the connection probability of pairs of disconnected nodes. Then a ranked list in decreasing order of scores is obtained and links at the top of list are most likely to appear (LIBEN-NOWELL;KLEINBERG, 2007;WANG et al, 2014;VALVERDE-REBAZA et al, 2016).…”