Mining ‘Following’ Patterns from Big but Sparsely Distributed Social Network Data
Carson Kai-Sang Leung, Ryan Middleton, Adam G.M. Pazdor, Yeyoung Won · 2018
In the current era of big data, advanced technology has led to easy collection or generation of high volumes of a wide variety of valuable data of different veracity. As rich sources of big data, social networks consist of users (or social entities) who are often linked by some interdependency such as `following' relationships. Since these big social networks keep growing at a high velocity, there are situations in which an individual user (or business) wants to find those frequently followed groups of social entities so that he can also 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 can be sparsely distributed (with most users only know some but not all users/social entities in some portions of a social network). In this paper, we present a social network mining algorithm that uses different compressed models to space-efficiently represent social entities so as to facilitate the discovery of groups of frequently followed social entities from these big but sparsely distributed social networks. Evaluation results show the practicality of our algorithm in efficient mining of `following' patterns from big but sparsely distributed social networks.