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.
CITATION STYLE
Rossetti, G., Milli, L., & Cazabet, R. (2019). CDLIB: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science, 4(1). https://doi.org/10.1007/s41109-019-0165-9
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