Efficient Mining of 'Following' Patterns from Very Big but Sparse Social Networks

Advances in technology in the current era of big data has led to the high-velocity 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. Given 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 very big (with lots of users/social entities) but can be sparse (with most users only know some but not all users/social entities in a social network). In this paper, we present a few social network mining algorithms that use compressed models in mining these very big but sparse social networks for discovering groups of frequently followed social entities. Evaluation results show the practicality of our algorithms in efficient mining of 'following' patterns from very big but sparse social networks.