Influential Nodes Identification Based on Activity Behaviors and Network Structure With Personality Analysis in Egocentric Online Social Networks

Influential Nodes Identification Based on Activity Behaviors and Network Structure With Personality Analysis in Egocentric Online Social Networks

Dhrubasish Sarkar (Amity University Kolkata, Kolkata, India), Soumyadeep Debnath (Tata Consultancy Services Limited, Kolkata, India), Dipak K. Kole (Department of CSE, Jalpaiguri Government Engineering College, Jalpaiguri, India) and Premananda Jana (Netaji Subhas Open University, Kalyani, India)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/IJACI.2019100101
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Online social network (OSN) platforms have become a place where the users share their details through various activities. Nowadays, OSNs have major impact not only on the users' lives but also on business and society. Organizations use OSN platforms to reach the target audiences and influential nodes may perform a major role to reach the target audience effectively and efficiently. In this article, a model has been proposed to identify influential nodes for a user in egocentric online social networks by considering the activity behaviors of the user and its network members along with network structure of the user. Finally, the personalities of both the user and the influencers has been analyzed using sentiment analysis and hashtag terms analysis. This article has derived a novel and efficient influence measurement model to evaluate an influence factor of each influential interacted node in the user's network for any of the classified social fields with sentiment types. To achieve that, the influence measurement process has been divided into three different categories namely behavioral influence, structural influence, and collaborative influence which is derived from the first two. Finally, the model for personality analysis has been incorporated.
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Influential nodes identification in OSNs has got attention of the researchers because of its significant business applications. Various techniques and algorithms have been proposed to identify influential users in a network.

First study in this field was carried out by Domingous and Richardson (2001). It has been considered as an algorithmic problem and the social networks have been represented as Markov random field in their work. They developed three algorithms for determining influential users which to be used for viral marketing. Later, few other Models have been proposed to identify influential users.

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