Name
Abstract | ||
|---|---|---|
A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1002/9781119483298.ch4 | arXiv: Social and Information Networks |
Field | DocType | Volume |
Network science,Community structure,Computer science,Artificial intelligence,Machine learning | Journal | abs/1712.06468 |
Citations | PageRank | References |
0 | 0.34 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Rosvall | 1 | 208 | 15.51 |
| Jean-Charles Delvenne | 2 | 299 | 32.41 |
| Michael T. Schaub | 3 | 63 | 9.90 |
| Renaud Lambiotte | 4 | 920 | 64.98 |