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).
[PDF]
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.
