Mining ‘following’ patterns from big sparse social networks
Carson Kai-Sang Leung, Edson M. Dela Cruz, Trevor L. Cook, Fan Jiang · 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · 2016
In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users (or social entities) in these social networks are often linked by some interdependency such as friendship or `following' relationships. As these big social networks keep growing, there are situations in which an individual user (or business) wants to find those frequently followed groups of social entities so that he can follow the same groups. Discovery of these frequently followed groups can be challenging because the social networks are usually big (with lots of users/social entities) but sparse (with most users only know some but not all users/social entities in a social network). In this paper, we present a data analytic solution that uses a compression model in mining these big but sparse social networks for discovering groups of frequently followed social entities. Evaluation results show the efficiency and practicality of our data analytic solution in discovering `following' patterns from social networks.