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.

Dhinesh Babu L-D

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Raj E.D

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

2015 International Conference on Computing and Network Communications (CoCoNet)

Across the globe, right from people who are technologically naive to people who are with high-technical knowledge, use, share, spread, and connect with each other through online social networks. People start believing and sharing the social network content without any proof of its authenticity. In several cases, the reliability of the information that gets shared between the users remains questionable due to the anonymity of the information creators. In this work, a fuzzy detection algorithm is proposed to identify the trustable content in social media. The proposed methodology is evaluated on Twitter social network platform. By computing the predictive measures, the efficiency of the proposed approach is well established. © The Institution of Engineering and Technology 2019

Fulltext

IET Networks

Fuzzy-based fake information detection algorithm to define the user trust on the content of social networks

With almost all educated individuals having access to computing cum communication gadgets like mobiles, tablets, PCs, and laptops, online social networks (OSNs) have become the default means of networking among majority of individuals. OSNs have become an inseparable part of daily lives attracting more than one third of the current world population. Majority of the users enjoy the entertainment aspects of OSNs like gaming with friends from different geographic locations. Social gaming has spawned a whole new sub culture which helps users to discover and build connections with other users. Game development companies constantly try to publicise and attract new users using OSNs to enhance their revenues. In this context, we propose a new firefly inspired strategy to spread and disseminate games in OSNs and assist the gaming companies to decrease the acceptance-discontinuance anomaly. Collective behaviour in online social network is closely related to swarm intelligence techniques. We have also proposed a rewarding and efficient Firefly inspired QoS-based priority pricing model that will attract more users to play online games while using online social networks, thereby, enhancing the profits of the service providers and game developers. Copyright © 2018 Inderscience Enterprises Ltd.

International Journal of Bio-Inspired Computation

A firefly inspired game dissemination and QoS-based priority pricing strategy for online social network games

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

There has been lot of research endeavours in detecting overlapping community structures in complex networks. This paper concentrates on one of the most popular complex networks of recent times—online social networks. Some of the existing methodologies in community detection for online social networks are discussed. The goal of the proposed algorithm is to solve the stability–plasticity dilemma and to suggest a new technique for detecting modular and granular overlaps in community detection. The stability–plasticity dilemma is solved using a Fuzzy ART-inspired algorithm for overlapping community detection for detecting modular and granular overlaps. The algorithm is designed by making use of network measures such as vertex betweenness, betweenness centrality, and split betweenness. The validation metrics used for testing the algorithm were sensitivity, specificity, accuracy and normalized mutual information. The algorithm has been tested and validated using benchmark datasets and real network datasets and gives a cumulative performance of 3.1/4.0. © 2016, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg.

International Journal of Fuzzy Systems

Effective Detection of Modular and Granular Overlaps in Online Social Networks Using Fuzzy ART

There has been a surge in the usage and spread of online social networks in the past few years. This paper focuses on online social networks, which is the most prominent complex network in the modern world. People use online social networks for making friendships, learning, for entertainment and to review product items. This expanded impact of online social networks has prompted the rise of a research area named online social network analysis. This paper reflects the research aspects in the detection of influential nodes in online social networks with respect to topological properties, network generation models and analytical measures in online social networks. We have clearly presented the concepts, issues and future research directions in the influential node detection in online social network analysis. The factors and conditions for the evolution and growth of online social networks and the pre-requisites for generating a random graph for simulation of online social networks have been discussed. The paper gives a clear direction on the relation between computation of influential users and the properties, models and measures in online social networks. © 2017 Inderscience Enterprises Ltd.

International Journal of Web Based Communities

A survey on topological properties, network models and analytical measures in detecting influential nodes in online social networks

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.

Journal	Data powered by Typeset2015 International Conference on Computing and Network Communications (CoCoNet)
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Open Access	0