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Article Dans Une Revue Applied Network Science Année : 2019

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

Résumé

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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Dates et versions

hal-02197272 , version 1 (30-07-2019)

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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, 2019, 4, pp.52. ⟨10.1007/s41109-019-0165-9⟩. ⟨hal-02197272⟩
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