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  The graphlet spectrum

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4A9-B Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4AA-9
Genre: Conference Paper

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 Creators:
Kondor, R, Author
Shervashidze, N1, Author           
Borgwardt, KM1, Author           
Danyluk, Editor
A., Editor
Bottou, L., Editor
Littman, M., Editor
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2009-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-605-58516-1
URI: http://www.cs.mcgill.ca/~icml2009/
DOI: 10.1145/1553374.1553443
BibTex Citekey: 5913
 Degree: -

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Title: 26th International Conference on Machine Learning (ICML 2009)
Place of Event: Montreal, Canada
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Title: 26th International Conference on Machine Learning (ICML 2009)
Source Genre: Proceedings
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Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 529 - 536 Identifier: -