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  Efficient Graphlet Kernels for Large Graph Comparison

Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient Graphlet Kernels for Large Graph Comparison. In D. Van Dyk, & M. Welling (Eds.), Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009) (pp. 488-495). Cambridge, MA, USA: MIT Press.

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 Creators:
Shervashidze, N1, 2, Author           
Vishwanathan , SVN, Author
Petri, TH, Author
Mehlhorn, K, Author
Borgwardt, KM1, 2, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              

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 Abstract: State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting it graphlets}, ie subgraphs with k nodes where k in { 3, 4, 5 . Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.

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 Dates: 2009-04
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 5664
 Degree: -

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Title: Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009)
Place of Event: Clearwater Beach, FL, USA
Start-/End Date: 2009-04-16 - 2009-04-18

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Title: Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009)
Source Genre: Proceedings
 Creator(s):
Van Dyk, D, Editor
Welling, M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 488 - 495 Identifier: -