Name
Abstract | ||
|---|---|---|
Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. These include scale-free degree distributions, small-world structure and assortative mixing by degree, which are also the properties captured by different random graph models proposed in the literature. However, many (non-social) real-world networks are in fact disassortative by degree. Thus, we here propose a simple evolving model that generates networks with most common properties of real-world networks including degree disassortativity. Furthermore, the model has a natural interpretation for citation networks with different practical applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2487788.2487987 | WWW (Companion Volume) |
Keywords | DocType | Volume |
measurement,theory,clustering,complex networks | Journal | abs/1303.5857 |
ISSN | ISBN | Citations |
Proceedings of the WWW Workshop on Large Scale Network Analysis
2013 (LSNA '13), pp. 527-530 | 978-1-4503-2038-2 | 10 |
PageRank | References | Authors |
0.68 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lovro Subelj | 1 | 209 | 16.37 |
| Marko Bajec | 2 | 465 | 34.56 |