Parallel social network mining for interesting 'following' patterns

Social networking sites (e.g., Facebook, Google+, and Twitter) have become popular for sharing valuable knowledge and information among social entities (e.g., individual users and organizations), who are often linked by some interdependency such as friendship. As social networking sites keep growing, there are situations in which a user wants to find those frequently followed groups of social entities so that he can follow the same groups. In this article, we present (i) a space-efficient bitwise data structure for capturing interdependency among social entities; (ii) a time-efficient data mining algorithm that makes the best use of our proposed data structure for serial discovery of groups of frequently followed social entities; and (iii) another time-efficient data mining algorithm for concurrent computation and discovery of groups of frequently followed social entities in parallel so as to handle high volumes of social network data. Evaluation results show the efficiency and practicality of our data structure and social network data mining algorithms. Copyright (C) 2016 John Wiley & Sons, Ltd.