The graphlet spectrum

Kondor, R; Shervashidze, N; Borgwardt, KM

doi:10.1145/1553374.1553443

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The graphlet spectrum

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Shervashidze, N
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Borgwardt, KM
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1553374.1553443
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引用

Kondor, R., Shervashidze, N., & Borgwardt, K. (2009). The graphlet spectrum. In A., Danyluk, L., Bottou, & M., Littman (Eds.), ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning (pp. 529-536). New York, NY, USA: ACM Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-C4A9-B

要旨

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.