CDLIB: a python library to extract, compare and evaluate communities from complex networks
| Title | CDLIB: a python library to extract, compare and evaluate communities from complex networks |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Rossetti, G, Milli, L, Cazabet, R |
| Journal | Applied Network Science |
| Volume | 4 |
| Pagination | 52 |
| Date Published | 2019/07/29 |
| ISBN Number | 2364-8228 |
| 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. |
| URL | https://link.springer.com/article/10.1007/s41109-019-0165-9 |
| DOI | 10.1007/s41109-019-0165-9 |
| Short Title | Applied Network Science |
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