Szczegóły publikacji
Opis bibliograficzny
A method of analysis and visualization of structured datasets based on centrality information / Wojciech CZECH, Radosław ŁAZARZ // W: Artificial intelligence and soft computing : 15th international conference, ICAISC 2016 : Zakopane, Poland, June 12–16, 2016 : proceedings, Pt. 2 / eds. Leszek Rutkowski, [et al.]. — Switzerland : Springer International Publishing, cop. 2016. — (Lecture Notes in Artificial Intelligence ; ISSN 0302-9743 ; 9693). — ISBN: 978-3-319-39383-4; e-ISBN: 978-3-319-39384-1. — S. 429–441. — Bibliogr. s. 440–441, Abstr. — Toż na Dysku Flash
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 98341 |
|---|---|
| Data dodania do BaDAP | 2016-06-23 |
| DOI | 10.1007/978-3-319-39384-1_37 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 15th International Conference on Artificial Intelligence and Soft Computing |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
We present a new method of quantitative graph analysis and visualization based on vertex centrality measures and distance matrices. After generating distance k-graphs and collecting frequency information about their vertex descriptors, we obtain generic, multidimensional representation of a graph, invariant to graph isomorphism. The histograms of vertex centrality measures, organized in a form of B-matrices, allow to capture subtle changes in network structure during its evolution and provide robust tool for graph comparison and classification. We show that different types of B-matrices and their extensions are useful in graph analysis tasks performed on benchmark complex networks from Koblenz and IAM datasets. We compare the results obtained for proposed Bmatrix extensions with performance of other state-of-art graph descriptors showing that our method is superior to others. © Springer International Publishing Switzerland 2016.