TY - JOUR T1 - CDLIB: a python library to extract, compare and evaluate communities from complex networks JF - Applied Network Science Y1 - 2019 A1 - Giulio Rossetti A1 - Letizia Milli A1 - Cazabet, Rémy AB - 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. VL - 4 SN - 2364-8228 UR - https://link.springer.com/article/10.1007/s41109-019-0165-9 JO - Applied Network Science ER -