2015 DeepGraphKernels

• (Yanardag & Vishwanathan, 2015) ⇒ Pinar Yanardag, and S.V.N. Vishwanathan. (2015). “Deep Graph Kernels.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783417

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Deep+Graph+Kernels
• http://dl.acm.org/citation.cfm?id=2783258.2783417&preflayout=flat#citedby

Quotes

Author Keywords

• Bioinformatics; collaboration networks; data mining; deep learning; graph kernels; r-convolution kernels; social networks; string kernels; structured data

Abstract

In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DeepGraphKernels	S.V.N. Vishwanathan Pinar Yanardag			Deep Graph Kernels				10.1145/2783258.2783417		2015