Mining interesting "following" patterns from social networks

Abstract

Over the past few years, social network sites (e.g., Facebook, Twitter, Weibo) have become very popular. These sites have been used for sharing knowledge and information among users. Nowadays, it is not unusual for any user to have many friends (e.g., hundreds or even thousands friends) in these social networks. In general, social networks consist of social entities that are linked by some interdependency such as friendship. As social networks keep growing, it is not unusual for a user to find those frequently followed groups of social entities in the networks so that he can follow the same groups. In this paper, we propose (i) a space-efficient bitwise data structure to capture interdependency among social entities and (ii) a time-efficient data mining algorithm that makes the best use of our proposed data structure to discover groups of friends who are frequently followed by social entities in the social networks. Evaluation results show the efficiency of our data structure and mining algorithm. © 2014 Springer International Publishing.