@article {1407,
	title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks},
	journal = {Applied Network Science},
	volume = {4},
	year = {2019},
	month = {2019/07/29},
	pages = {52},
	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.},
	isbn = {2364-8228},
	doi = {https://doi.org/10.1007/s41109-019-0165-9},
	url = {https://link.springer.com/article/10.1007/s41109-019-0165-9},
	author = {Giulio Rossetti and Letizia Milli and Cazabet, R{\'e}my}
}
