By Topic

SFLOSCAN: A biologically-inspired data mining framework for community identification in dynamic social networks

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
$33 $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)
Abdelghani Bellaachia ; Computer Science Department, The George Washington University, Washington DC, USA ; Anasse Bari

In this paper we present the first biologically inspired framework for indentifying communities in dynamic social networks. Community detection in a social network is a complex problem when interactions among members change over time. Existing community identification algorithms are limited to evaluating a snapshot of a social network at a specific time. Our algorithm evaluates social interactions as they occur over time. The user can see the detected communities at any given time. We propose a relatively simple, scalable, and novel artificial life-based algorithm named “SFloscan”. This algorithm is based on the natural phenomena of bird flocking. We model a social network as an artificial life where members flock together in a virtual two-dimensional space to form communities. We demonstrate empirically that our algorithm outperforms and overcomes the limitations of the algorithms used for community detection. We analyze the performance of SFloscan using datasets widely used in the real world.

Published in:

Swarm Intelligence (SIS), 2011 IEEE Symposium on

Date of Conference:

11-15 April 2011