A Model for Social Communication Network in Mobile Instant Messaging Systems Mobile instant messaging (MIM) systems have provided very convenient ways for communication and information exchange in recent years. Interesting features of these applications have made them important platforms for spreading information, idea, behavior, and rumor. In contrast to traditional social networks, communication in MIM applications is not necessarily based on friendship relations. In groupbased communication and broadcasting in channels that are prominent features of MIM systems, users may send messages to and receive messages from people with whom do not already have friendly relations. This kind of communication leads to the creation of special network in which not only users and their contact lists but also groups and channels are involved. Existing complex network models are not sufficiently expressive to represent this kind of communication network. In this article, we introduce the concept of social communication network (SCN) to be able to consider special structural properties of communications in MIM systems and propose a model for representing and generating the SCN in MIM systems. The proposed model covers all social communications between users, groups, and channels and exhibits the statistics observed in realworld data. We also redefine some existing properties and introduce some new properties of the MIM network that earlier models of complex networks do not capture. To evaluate the proposed model, we conduct a number of simulation experiments on the model and compare the results with a realworld graph that we have extracted from Telegram. The results show that SCN derived by the model is highly compatible with the realworld graph. The proposed model provides a useful basis for analysis and evaluation of MIM network properties.

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.