Graph Embedding Using Constant Shift Embedding

Salim Jouili 1 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In the literature, although structural representations (e.g. graph) are more powerful than feature vectors in terms of representational abilities, many robust and efficient methods for classification (unsupervised and supervised) have been developed for feature vector representations. In this paper, we propose a graph embedding technique based on the constant shift embedding which transforms a graph to a real vector. This technique gives the abilities to perform the graph classification tasks by procedures based on feature vectors. Through a set of experiments we show that the proposed technique outperforms the classification in the original graph domain and the other graph embedding techniques.
Keywords : Structural pattern recognition Graph embedding : Graph classification
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Chapitre d'ouvrage
D. Ünay, Z. Cataltepe, and S. Aksoy. International Conference on Pattern Recognition - ICPR 2010, 6388, Springer Berlin / Heidelberg, pp.83-92, 2010, Lecture Notes in Computer Science
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Dernière modification le : mardi 24 avril 2018 - 13:37:18
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Salim Jouili, Salvatore Tabbone. Graph Embedding Using Constant Shift Embedding. D. Ünay, Z. Cataltepe, and S. Aksoy. International Conference on Pattern Recognition - ICPR 2010, 6388, Springer Berlin / Heidelberg, pp.83-92, 2010, Lecture Notes in Computer Science. 〈inria-00526993〉

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