Social network analysis using big data

O. Liu, K. L. Man, W. Chong, C. O. Chan

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.

Original languageEnglish
Title of host publicationIMECS 2016 - International Multiconference of Engineers and Computer Scientists 2016
EditorsDavid Dagan Feng, Alexander M. Korsunsky, S. I. Ao, Craig Douglas, Oscar Castillo
Pages639-640
Number of pages2
Volume2
ISBN (Electronic)9789881404763
Publication statusPublished - 18 Mar 2016
EventInternational Multiconference of Engineers and Computer Scientists 2016, IMECS 2016 - Tsimshatsui, Kowloon, Hong Kong
Duration: 16 Mar 201618 Mar 2016

Conference

ConferenceInternational Multiconference of Engineers and Computer Scientists 2016, IMECS 2016
CountryHong Kong
CityTsimshatsui, Kowloon
Period16/03/1618/03/16

Keywords

  • Big data technologies
  • Directed network
  • Social network

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