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Classification of large Graphs by a local Tree decomposition

Emmert-Streib, Frank and Dehmer, Matthias and Kilian, Jürgen (2005):
Classification of large Graphs by a local Tree decomposition.
In: Proceeedings of DMIN'05, International Conference on Data Mining, pp. 200-207, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2005
Creators: Emmert-Streib, Frank and Dehmer, Matthias and Kilian, Jürgen
Title: Classification of large Graphs by a local Tree decomposition
Language: German
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.

Title of Book: Proceeedings of DMIN'05, International Conference on Data Mining
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Date Deposited: 31 Dec 2016 12:59
Identification Number: EDJ:2005
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