Efficient Graphlet Kernels for Large Graph Comparison
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 graphlets, i.e., 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.
Cite
Text
Shervashidze et al. "Efficient Graphlet Kernels for Large Graph Comparison." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Shervashidze et al. "Efficient Graphlet Kernels for Large Graph Comparison." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/shervashidze2009aistats-efficient/)BibTeX
@inproceedings{shervashidze2009aistats-efficient,
title = {{Efficient Graphlet Kernels for Large Graph Comparison}},
author = {Shervashidze, Nino and Vishwanathan, Svn and Petri, Tobias and Mehlhorn, Kurt and Borgwardt, Karsten},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {488-495},
volume = {5},
url = {https://mlanthology.org/aistats/2009/shervashidze2009aistats-efficient/}
}