2019
Journal article  Open Access

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

Rossetti G., Milli L., Cazabet R.

Community detection framework  Social network analysis  Community discovery library 

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.

Source: Applied network science 4 (2019). doi:10.1007/s41109-019-0165-9

Publisher: Springer international, Cham, Svizzera



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BibTeX entry
@article{oai:it.cnr:prodotti:415651,
	title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks},
	author = {Rossetti G. and Milli L. and Cazabet R.},
	publisher = {Springer international, Cham, Svizzera},
	doi = {10.1007/s41109-019-0165-9},
	journal = {Applied network science},
	volume = {4},
	year = {2019}
}