Efficient graph kernels by randomization

Authors:
Marion Neumann;Novi Patricia;Roman Garnett;Kristian Kersting
Affiliations:
Knowledge Discovery Department, Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany;Knowledge Discovery Department, Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States;Knowledge Discovery Department, Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Year:
2012

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Abstract

Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. In this paper, we explore the power of continuous node-level features for propagation-based graph kernels. Specifically, propagation kernels exploit node label distributions from propagation schemes like label propagation, which naturally enables the construction of graph kernels for partially labeled graphs. In order to efficiently extract graph features from continuous node label distributions, and in general from continuous vector-valued node attributes, we utilize randomized techniques, which easily allow for deriving similarity measures based on propagated information. We show that propagation kernels utilizing locality-sensitive hashing reduce the runtime of existing graph kernels by several orders of magnitude. We evaluate the performance of various propagation kernels on real-world bioinformatics and image benchmark datasets.