syslog
15Jul/110

Socio-spatial properties of online social networks

Posted by Salvatore Scellato

Some social scientists have suggested that the advent of fast long-distance travel and cheap online communication tools might have caused the "death of distance": as described by Frances Cairncross, the world appears shrinking as individuals connect and interact with each other regardless of the geographic distances which separates them. Unfortunately, the lack of reliable geographic data about large-scale social networks has hampered research on this specific problem.

However, the recent growing popularity of location-based services such as Foursquare and Gowalla has unlocked large-scale access to where people live and who their friends are, making possible to understand how distance and friendship ties relate to each other.

In a recent paper which will appear at the upcoming ICWSM 2011 conference we study the socio-spatial properties arising between users of three large-scale online location-based social networks. We discuss how distance still matters: individuals tend to create social ties with people living nearby much more likely than with persons further away, even though strong heterogeneities still appear across different users.

30May/112

Place-Friends: designing a link prediction system for location-based services

Posted by Salvatore Scellato

Online social networks often deploy friend recommending systems, so that new users can be discovered and new social connections can be created. Since these service have easily millions of users, recommending friends potentially involves searching a huge prediction space: this is why platforms such as Facebook, LinkedIn and Twitter merely focus their prediction efforts on friends-of-friends, that is, on users that are only 2 hops away in the social network, sharing at least a common friend. Extending prediction efforts beyond this social circle is simply not worth it.

017/365 areyoucheckedin?

Nonetheless, in location-based social networks there is an unprecedented source of potential promising candidates for recommending new friends: the places where user check-in at.  In a recent paper which will appear at the upcoming ACM SIGKDD 2011 conference we address the problem of designing a link prediction system which exploits the properties of the places that users visit.