A Theoretical Approach for Discovery of Friends from Directed Social Graphs

Since social networking has been popular in the current era of big data, numerous social networking sites (e.g., Instagram, Twitter) have generated huge volumes of social data at a rapid rate. Embedded into these data are valuable information and knowledge. This calls for social network analysis and mining. In this paper, we specifically aim to discover interesting relationships in directed social graphs via a theoretical approach. More specifically, we examine both graph theory and linear algebra approaches to discover interesting entities (e.g., popular followees, second-degree followees) from social networks represented in the form of big directional graphs.