A Concise Social Network Representation with Flow Hierarchy Using Frequent Interactions

In this paper we introduce the flow hierarchy derived from frequent interactions to represent complex social networks in a concise way. Frequent interactions extract the impactful users while the flow hierarchy visualizes the dependencies and identifies different roles such as leaders, topic experts, information disseminators, emerging leaders and active followers. It is highly applicable in intelligent systems which involve user ranking, expert searching, recommendation, viral marketing, political campaigning, disaster management and many more. We present novel methods of deriving flow hierarchy considering the temporal dimension of interactions among users, flow directions and structural dependencies. We empirically evaluate proposed methods using real-world social network interaction datasets related to citation and retweet networks. Empirical analysis reveals that a hierarchy derived from frequent social interactions is effective in extracting the impactful users and their position in the network. Baseline results with user-centric measures show the efficacy of the proposed methods in finding a concise network representation.