A
Andrea Lancichinetti
Researcher at Umeå University
Publications - 28
Citations - 12091
Andrea Lancichinetti is an academic researcher from Umeå University. The author has contributed to research in topics: Cluster analysis & Complex network. The author has an hindex of 19, co-authored 28 publications receiving 10809 citations. Previous affiliations of Andrea Lancichinetti include Polytechnic University of Turin & Institute for Scientific Interchange.
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Benchmark graphs for testing community detection algorithms
TL;DR: This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.
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Community detection algorithms: a comparative analysis.
TL;DR: Three recent algorithms introduced by Rosvall and Bergstrom and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
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Detecting the overlapping and hierarchical community structure in complex networks
TL;DR: The first algorithm that finds both overlapping communities and the hierarchical structure is presented, based on the local optimization of a fitness function, enabling different hierarchical levels of organization to be investigated.
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Finding Statistically Significant Communities in Networks
TL;DR: OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.
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Detecting the overlapping and hierarchical community structure of complex networks
TL;DR: In this article, the authors proposed a method based on local optimization of a fitness function to find overlapping communities and the hierarchical structure of complex networks, where community structure is revealed by peaks in the fitness histogram, which can be tuned by a parameter enabling to investigate different hierarchical levels of organization.