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The graphlet spectrum

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84919

Shervashidze,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Kondor, R., Shervashidze, N., & Borgwardt, K. (2009). The graphlet spectrum. In 26th International Conference on Machine Learning (ICML 2009) (pp. 529-536). New York, NY, USA: ACM Press.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C4A9-B
Zusammenfassung
Current graph kernels suffer from two limitations: graph kernels based on counting particular types of subgraphs ignore the relative position of these subgraphs to each other, while graph kernels based on algebraic methods are limited to graphs without node labels. In this paper we present the graphlet spectrum, a system of graph invariants derived by means of group representation theory that capture information about the number as well as the position of labeled subgraphs in a given graph. In our experimental evaluation the graphlet spectrum outperforms state-of-the-art graph kernels.