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Conference Paper

The graphlet spectrum

MPS-Authors
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Shervashidze,  N
Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Developmental Biology, Max Planck Society;

External Resource

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

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.


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