Online Social networks have become popular with the increased use of mobile devices. Online Social network analysis alleviates the quantitative as well as qualitative analysis of online social networks. Online social networks deals with human emotions and communications, the vagueness in the process is more and is a hindrance in computation. We analyze two network measures namely centrality and prestige by developing a fuzzy rule based inference system. We obtained the relation between interaction, centrality and prestige in online social networks. The paper describes in detail, the need for a fuzzy approach in online social network analysis and analyzes the network measures. © 2016 ACM.

Dhinesh Babu L-D

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Raj E.D

Ariwa E.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A Fuzzy Approach to Centrality and Prestige in Online Social Networks

Proceedings of the International Conference on Informatics and Analytics - ICIA-16

Centrality in social network is one of the major research topics in social network analysis. Even though there are more than half a dozen methods to find centrality of a node, each of these methods has some drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This new computation technique makes use of parameters that are more realistic and practical in online social networks. The interactions between nodes play a significant role in determining the centrality of node. The new method has been calculated both empirically as well as experimentally. The new method is tested, verified and validated for different sets of random networks and benchmark datasets. The method has been correlated with other state of the art centrality measures. The new centrality measure is found to be realistic and suits well with online social networks. The proposed method can be used in applications such as finding the most prestigious node and for discovering the node which can influence maximum number of users in an online social network. FBCS centrality has higher Kendall's tau correlation when compared with other state of the art centrality methods. The robustness of the FBCS centrality is found to be better than other centrality measures. © 2017 Elsevier B.V.

Applied Soft Computing

Flocking based evolutionary computation strategy for measuring centrality of online social networks

Online social networks have gained importance in the past few years and have become an inseparable form of communication. People use online social networks for making friends, gaining knowledge, for entertainment and to review products. This increased influence of social networks has led to the emergence of a research area named online social network analysis. In this paper we propose a model fuzzy inference system that analyze and quantify the network measures used in the analysis of online social networks. We have divided the network measures into two classes namely connection based measures and network structure based measure. We have linked the connection based measures with the network structure based measure and have produced a fuzzy inference system for online social network analysis. © 2015 IEEE.

2015 International Conference on Computing and Network Communications (CoCoNet)

A model fuzzy inference system for online social network analysis

Social networking generates a huge amount of data which can throw useful insights if processed at the right time. Social computing is another emerging technique where social networking is made use for computational purpose. Cloud storage plays an important role in social computing. Privacy in social computing is still a debated issue. The privacy concerns and possible loopholes are discussed in detail. In this paper we are proposing some mathematical models to compute the probability of staying in social network and proposing an algorithm named firefly inspired algorithm for establishing connections (FIAEC). Copyright © 2015 Inderscience Enterprises Ltd.

International Journal of Communication Networks and Distributed Systems

A firefly swarm approach for establishing new connections in social networks based on big data analytics

Encyclopedia of Social Network Analysis and Mining

Location-Based Online Social Networks: Location-Based Online Social Media, Location-Based Online Social Services

Relational and Algebraic Methods in Computer Science Lecture Notes in Computer Science

Social Networks: Prestige, Centrality, and Influence

International Journal of Approximate Reasoning

A theoretical investigation of regular equivalences for fuzzy graphs

Journal	Data powered by TypesetProceedings of the International Conference on Informatics and Analytics - ICIA-16
Publisher	Data powered by TypesetACM Press
Open Access	0