<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Letizia Milli</style></author><author><style face="normal" font="default" size="100%">Cazabet, Rémy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CDLIB: a python library to extract, compare and evaluate communities from complex networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Network Science</style></secondary-title><short-title><style face="normal" font="default" size="100%">Applied Network Science</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019/07/29</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s41109-019-0165-9</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">52</style></pages><isbn><style face="normal" font="default" size="100%">2364-8228</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>