Finding community structure in networks using the eigenvectors of matrices
| dc.creator | Newman, M. E. J. | |
| dc.date | 2006-05-10 | |
| dc.date | 2006-07-23 | |
| dc.date.accessioned | 2026-07-07T07:14:57Z | |
| dc.date.available | 2026-07-07T07:14:57Z | |
| dc.description | We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks. | |
| dc.description | 22 pages, 8 figures, minor corrections in this version | |
| dc.identifier | https://arxiv.org/abs/physics/0605087 | |
| dc.identifier | http://arxiv.org/abs/physics/0605087 | |
| dc.identifier | Phys. Rev. E 74, 036104 (2006) | |
| dc.identifier | doi:10.1103/PhysRevE.74.036104 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/113096 | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Physics and Society | |
| dc.title | Finding community structure in networks using the eigenvectors of matrices | |
| dc.type | text |