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Community detection algorithms: a comparative analysis

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If you have a question about this talk, please contact Eiko Yoneki.

Uncovering the community structure exhibited by real networks is a crucial step towards an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Here we test several methods against a recently introduced class of realistic benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.

Bio: I got my University degree at the university of Catania (Italy) in 1995 and my PhD in Germany, Physics Department of the University of Bielefeld, in 2000. After 4 further years in Bielefeld as a postdoctoral research associate, I changed field (moving to complex systems from statistical filed theory) and joined Alessandro Vespignani’s group at the School of Informatics of Indiana University where I stayed as a postdoc from 2005 till 2007. Since 2007 I am research scientist at the Institute for Scientific Interchange of Torino, where a few months ago I have become leader of my own research group, called Sociophysics Lab.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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