Resolution limit in community detection
| dc.creator | Fortunato, Santo | |
| dc.creator | Barthelemy, Marc | |
| dc.date | 2006-07-11 | |
| dc.date | 2006-07-14 | |
| dc.date.accessioned | 2026-07-07T07:37:22Z | |
| dc.date.available | 2026-07-07T07:37:22Z | |
| dc.description | Detecting community structure is fundamental to clarify the link between structure and function in complex networks and is used for practical applications in many disciplines. A successful method relies on the optimization of a quantity called modularity [Newman and Girvan, Phys. Rev. E 69, 026113 (2004)], which is a quality index of a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total number L of links of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. The probability that a module conceals well-defined substructures is the highest if the number of links internal to the module is of the order of \sqrt{2L} or smaller. We discuss the practical consequences of this result by analyzing partitions obtained through modularity optimization in artificial and real networks. | |
| dc.description | 8 pages, 3 figures. Clarification of definition of community in Section II + minor revisions | |
| dc.identifier | https://arxiv.org/abs/physics/0607100 | |
| dc.identifier | http://arxiv.org/abs/physics/0607100 | |
| dc.identifier | Proc. Natl. Acad. Sci. USA 104 (1), 36-41 (2007) | |
| dc.identifier | doi:10.1073/pnas.0605965104 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/120751 | |
| dc.subject | Physics and Society | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Resolution limit in community detection | |
| dc.type | text |