Name
Abstract | ||
|---|---|---|
Network clustering algorithms are typically based only on the topology information of the network. In this paper, we introduce traffic as a quantity representing the intensity of the relationship among nodes in the network, regardless of their connectivity, and propose an evolutionary clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. In a comparative evaluation based on synthetic instances and two real world datasets, we show that our approach outperforms a selection of well established evolutionary and non-evolutionary clustering algorithms. (C) 2013 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.asoc.2013.06.022 | Applied Soft Computing |
Keywords | DocType | Volume |
Clustering,Traffic matrices,Genetic algorithms | Journal | 13 |
Issue | ISSN | Citations |
11 | 1568-4946 | 6 |
PageRank | References | Authors |
0.46 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maurizio Naldi | 1 | 285 | 47.98 |
| Sancho Salcedo-Sanz | 2 | 580 | 71.21 |
| Leopoldo Carro-Calvo | 3 | 79 | 7.45 |
| Luigi Laura | 4 | 305 | 36.85 |
| Antonio Portilla-Figueras | 5 | 147 | 19.07 |
| Giuseppe F. Italiano | 6 | 2364 | 254.07 |