Abstract
A substantial volume of research has been devoted to studies of community structure in networks, but communities are not the only possible form of large-scale network structure. Here we describe a broad extension of community structure that encompasses traditional communities but includes a wide range of generalized structural patterns as well. We describe a principled method for detecting this generalized structure in empirical network data and demonstrate with real-world examples how it can be used to learn new things about the shape and meaning of networks.
| Original language | English |
|---|---|
| Article number | 088701 |
| Journal | Physical Review Letters |
| Volume | 115 |
| DOIs | |
| Publication status | Published - 1 May 2015 |
Keywords
- Computer Science - Social and Information Networks, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Physics - Data Analysis, Statistics and Probability, Physics - Physics and Society, Statistics - Machine Learning