Emmert-Streib, Frank ; Dehmer, Matthias ; Kilian, Jürgen (2005)
Classification of large Graphs by a local Tree
decomposition.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. This classifier is based on a local decomposition of the graph for each node in generalized trees. The obtained trees, forming the tree set of the graph, are then pairwise compared by a generalized treesimilarity- algorithm (GTSA) and the resulting similarity scores determine a characteristic similarity distribution of the graph. Classification in this context is defined as mutual consistency for all pure and mixed tree sets and their resulting similarity distributions in a graph class. We demonstrate the application of this method to an artificially generated data set and for data from microarray experiments of cervical cancer.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2005 |
| Autor(en): | Emmert-Streib, Frank ; Dehmer, Matthias ; Kilian, Jürgen |
| Art des Eintrags: | Bibliographie |
| Titel: | Classification of large Graphs by a local Tree decomposition |
| Sprache: | Deutsch |
| Publikationsjahr: | 2005 |
| Buchtitel: | Proceeedings of DMIN'05, International Conference on Data Mining |
| Kurzbeschreibung (Abstract): | We present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. This classifier is based on a local decomposition of the graph for each node in generalized trees. The obtained trees, forming the tree set of the graph, are then pairwise compared by a generalized treesimilarity- algorithm (GTSA) and the resulting similarity scores determine a characteristic similarity distribution of the graph. Classification in this context is defined as mutual consistency for all pure and mixed tree sets and their resulting similarity distributions in a graph class. We demonstrate the application of this method to an artificially generated data set and for data from microarray experiments of cervical cancer. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation |
| Hinterlegungsdatum: | 31 Dez 2016 12:59 |
| Letzte Änderung: | 03 Jun 2018 21:29 |
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