• Aug 09, 2018 News![CFP] The annual meeting of IJFCC Editorial Board, ICCTD 2019, will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • Aug 09, 2018 News!IJFCC Vol. 6, No. 1-No. 3 has been indexed by EI (Inspec).   [Click]
  • Dec 24, 2018 News!The papers published in Vol.7, No.1-No.2 have all received dois from Crossref.
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2018 Vol.7(4): 98-102 ISSN: 2010-3751
DOI: 10.18178/ijfcc.2018.7.4.528

A Technique to Identify Key Players that Helps to Improve Businesses Using Multilayer Social Network Analysis

Aftab Farooq, Usman Akram, Gulraiz Javaid Joyia, and Chaudhry Naeem Akbar
Abstract—Social network analysis has gained importance in the current era of online networks. Companies nowadays are using the social network analysis tools and techniques to enhance their business positions. Analyzing the social networks provide the massive amount of useful information for the business. In this paper, we have analyzed the online social networks of leading mobile phone company Huawei. The importance of any individual (Node) can be determined by the participation and behavior of the node in the OSN. Centrality measure is the key factor to visualize any social network. Centrality in term includes Density, Closeness Centrality, Degree Centrality, Eigenvector Centrality and some other metrics such as Coefficient Clustering, Page Rank. These metrics can be used to determine the most important node (key player) in a network. Visualization of the social network is a complicated task as it is expanded every minute as thousands of users join the social network daily. We have put an effort to analyze the social network and determine the influential node with the calculation of above-mentioned metrics. The overall aim of our research is to improve the business by identifying the most influential node. The experimental results and a detailed quantitative analysis show that this is the more efficient and effective way to detect the influential nodes in an online social network.

Index Terms—Social network analysis (SNA), data mining, community detection, online social network (OSN), online business communities, centrality measures, performance, efficiency, business improvement, crawling.

The authors are with the National University of Sciences and Technology Islamabad, Pakistan (e-mail: aftabfarooq2012@gmail.com, usmanakram@gmail.com, ingrgulraiz@gmail.com, chmnaeemakbar@yahoo.com).


Cite: Aftab Farooq, Usman Akram, Gulraiz Javaid Joyia, and Chaudhry Naeem Akbar, "A Technique to Identify Key Players that Helps to Improve Businesses Using Multilayer Social Network Analysis," International Journal of Future Computer and Communication vol. 7, no.4, pp. 98-102, 2018.

Copyright © 2008-2018. International Journal of Future Computer and Communication. All rights reserved.
E-mail: ijfcc@ejournal.net