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 realworld 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 usercentric measures show the efficacy of the proposed methods in finding a concise network representation.

A CommunityEnhanced Retrieval Model for TextRich Heterogeneous Information Networks In recent years, we have witnessed a large increase in the number of available textrich heterogeneous networks, in which text documents, users and other objects are interconnected in various ways. Examples include social networks, bibliographic information networks, and collaboration networks. Textrich heterogeneous networks contain text nodes (e.g., user comments in social networks; scientific publications in bibliographic networks), nontext nodes (e.g., users and companies in social networks), as well as rich relationships between different types of nodes. An important task, which is very useful on its own right, as well as often serves as a preprocessing to mining and analytical tasks, is identifying relevant text information from the heterogeneous information networks given user queries. Unlike Web search and many related problems, the effects of the underlying semantically meaningful patterns in heterogeneous networks play an important role in determining relevant answers for user search queries. For example, there are several structural patterns we can explore to more effectively identify relevant text nodes for a given query. The relevant nodes are likely to come from the same usertopic communities as the query. In addition, the relevant nodes on the same query topic tend to form tight local relationships with each other in the same usertopic community. In this paper, we present a unified and principled framework that effectively integrates network community and local relationship detection with retrieval model construction in a mutually enhancing manner, which leads to a communityenhanced retrieval model for textrich heterogeneous information networks. Experimental results show that this new model significantly outperforms stateoftheart baselines.