CDLIB: a python library to extract, compare and evaluate communities from complex networks

Abstract : Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library-namely CDLIB-designed to serve this need. The aim of CDLIB is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.
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Contributor : Remy Cazabet <>
Submitted on : Tuesday, July 30, 2019 - 11:23:36 AM
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Giulio Rossetti, Letizia Milli, Rémy Cazabet. CDLIB: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science, Springer, 2019, 4, pp.52. ⟨10.1007/s41109-019-0165-9⟩. ⟨hal-02197272⟩

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