Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm-Reference-Cited by-同舟云学术

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Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Published:2012-01 Issue:3 Volume:27 Page:455-467
ISSN:1000-9000
Container-title:Journal of Computer Science and Technology
language:en
Short-container-title:J. Comput. Sci. Technol.

Author:

Gong Mao-Guo,Zhang Ling-Jun,Ma Jing-Jing,Jiao Li-Cheng

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s11390-012-1235-y.pdf

Reference38 articles.

1. Greene D, Doyle D, Cunningham P. Tracking the evolution of communities in dynamic social networks. In Proc. Int. Conf. Advances in Social Networks Analysis and Mining, August 2010, pp.176–183.

2. Yang T B, Chi Y, Zhu S H, Gong Y H, Jin R. Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 2011, 82(2): 157–189.

3. Lin Y R, Chi Y, Zhu S H, Sundaram H, Tseng B L. Facetnet: A framework for analyzing communities and their evolutions in dynamic networks. In Proc. the 17th Int. Conf. World Wide Web, April 2008, pp.685–694.

4. Chakrabarti D, Kumar R, Tomkins A. Evolutionary clustering. In Proc. the 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2006, pp.554–560.

5. Folino F, Pizzuti C. A multiobjective and evolutionary clustering method for dynamic networks. In Proc. Int. Conf. Advances in Social Networks Analysis and Mining, August 2010, pp.256–263.

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