Paper Info
Title
Community detection algorithms: a comparative analysis: invited presentation, extended abstract
Abstract
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. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of 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.
Year
DOI
Venue
2009
10.5555/1698822.1698858
VALUETOOLS
Keywords
Field
DocType
excellent performance,community size,additional advantage,real network,real system,complex system,benchmark graph,artificial graph,community structure,comparative analysis,known community structure,community detection algorithm,networks
Complex system,Graph,Random graph,Computer science,Algorithm,Artificial intelligence,Real systems,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
978-963-9799-70-7
31
1.07
References 
Authors
2
2
Name
Order
Citations
PageRank
Santo Fortunato14209212.38
Andrea Lancichinetti251428.58