On Lipschitz Embeddings of Graphs

Authors:
Kaspar Riesen;Horst Bunke
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Year:
2008

Citing 0
Cited 2

Graph Classification Based on Dissimilarity Space Embedding

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph classification by means of Lipschitz embedding

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

In pattern recognition and related fields, graph based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph based machine learning, such as graph kernels or graph embedding techniques. These procedures overcome a major drawback of graphs, which consists in a serious lack of algorithms for classification and clustering. The present paper is inspired by the idea of representing graphs by means of dissimilarities and extends previous work to the more general setting of Lipschitz embeddings. In an experimental evaluation we empirically confirm that classifiers relying on the original graph distances can be outperformed by a classification system using the Lipschitz embedded graphs.