English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Efficient Graphlet Kernels for Large Graph Comparison

MPS-Authors
/persons/resource/persons84919

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

/persons/resource/persons75313

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

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C545-3
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.