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

You are here

TitleCDLIB: a python library to extract, compare and evaluate communities from complex networks
Publication TypeJournal Article
Year of Publication2019
AuthorsRossetti, G, Milli, L, Cazabet, R
JournalApplied Network Science
Volume4
Pagination52
Date Published2019/07/29
ISBN Number2364-8228
AbstractCommunity 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.
URLhttps://link.springer.com/article/10.1007/s41109-019-0165-9
DOI10.1007/s41109-019-0165-9
Short TitleApplied Network Science
Research Project: