@inproceedings{9f4208ccb3e74370925b917c2d666a9d,
title = "Social network analysis using big data",
abstract = "The increasing use ol social networks, such as Facebook, Twitter, and Weibo, has produced and is producing huge volume of data. Business firms and other organizations are interested in discovering new business insight to increase business performance. By using advanced analytics, enterprises can analyze big data to learn about relationships underlying social networks that characterize the social behavior of individuals and groups. Using data describing the relationships, we are able to identify social leaders who influence the behavior of others in the network, and on the other hand, to determine which people arc most affected by other network participants. This study focuses on modeling the knowledge diffusion In social networks. We will present a new evolving model of a directed, scale-free network. We will test the effectiveness of our model by a simulation using data of a real-world social network.",
keywords = "Big data technologies, Directed network, Social network",
author = "O. Liu and Man, {K. L.} and W. Chong and Chan, {C. O.}",
year = "2016",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "639--640",
editor = "Feng, {David Dagan} and Korsunsky, {Alexander M.} and Ao, {S. I.} and Craig Douglas and Oscar Castillo",
booktitle = "IMECS 2016 - International Multiconference of Engineers and Computer Scientists 2016",
note = "International Multiconference of Engineers and Computer Scientists 2016, IMECS 2016 ; Conference date: 16-03-2016 Through 18-03-2016",
}