Big Data Analytics of Social Networks for the Discovery of "Following" Patterns

Carson Kai-Sang Leung, Fan Jiang

Abstract: 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 individual users or businesses want to find those frequently followed groups of social entities so that they can follow the same groups. In this paper, we present a big data analytics solution that uses the MapReduce model to mine social networks for discovering groups of frequently followed social entities. Evaluation results show the efficiency and practicality of our big data analytics solution in discovering “following” patterns from social networks.