By Topic

Evolving social network analysis: A case study on mobile phone data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Baruah, R.D. ; Sch. of Comput. & Commun., Lancaster Univ., Lancaster, UK ; Angelov, P.

Mobile phone data can provide rich information on human activities and their social relationships which are dynamic in nature. Analysis of such social networks emerging from phone calls of mobile users can be useful in many aspects. In this paper we report the methods and results from a case study on the analysis of a social network from mobile phone data. The analysis involves tracking the dynamics of the network, identifying key individuals and their close associates, and identifying individuals having communication pattern similar to the key individuals. We introduce novel measures to quantify, the evolution in the network, significance of an individual, and social association of an individual. In order to group individuals having similar communication pattern, we applied recently proposed online clustering approach called eClustering (evolving clustering) due to its adaptive nature and low computational overhead. The results show the pertinence of the proposed quantification measures to analysis of evolving social network.

Published in:

Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on

Date of Conference:

17-18 May 2012