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 in these social networks are often linked by some interdependency such as friendship. As these big social networks keep growing, there are situations in which an individual user wants to find popular groups of friends so that he can recommend the same groups to other users. In this paper, we present a big data analytic solution that uses the MapReduce model in mining these big social networks for discovering groups of frequently connected users for friend recommendation. Evaluation results show the efficiency and practicality of our data analytic solution in mining big social networks, discovering popular users, and recommending friends.
['Fan Jiang', 'Carson Kai-Sang Leung', 'Adam G. M. Pazdor']
Big data mining of social networks for friend recommendation