Overview of Various Dynamic Community Evolution Detection Approaches

Authors

  • Anu Jose Department of Computer Science Engineering, Manipal Institute of Technology, Manipal, Karnataka, India
  • D. Chitraprasad Department of Computer Science Engineering, TKM College of Engineering, Kollam, Kerala, India
  • Simi P. Thomas Department of Electronics and Communication Engineering, Mangalam College of Engineering, Kottayam, Kerala, India

Keywords:

social network, dynamic community detection, graph metrics, degree centrality, event detection

Abstract

Social network in a simplest form is a social structure consisting of units that are connected by various kinds of relations like friendship, common interest, financial exchange, dislike, knowledge or prestige. This can be represented by a combination of graph theory and data mining concepts. The easiest way to present social network in a mathematical way is graph representation where members are nodes of the graph and relations are edges between those nodes. One of the most interesting research topics is the dynamics of social groups which means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. In this paper we will provide the overview of some approaches which identify the evolution of dynamic communities

Keywords: social network, dynamic community detection, graph metrics, degree centrality, event detection.

 

Cite this Article:Anu Jose, D. Chitraprasad, Simi P Thomas, Overview of Various Dynamic Community Evolution Detection Approaches. Research & Reviews: A Journal of Embedded System & Applications. 3(1): 28–32p.

Published

2015-04-22

Issue

Vol. 3 No. 1 (2015)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.