Faster Kernels for Graphs with Continuous Attributes via Hashing

TL;DR

This paper introduces hash graph kernels, a scalable framework that converts continuous graph attributes into discrete labels using hashing, enabling efficient kernel computation for large graphs with continuous attributes.

Contribution

The paper proposes a novel framework for scalable graph kernels with continuous attributes by applying randomized hashing to convert attributes into discrete labels.

Findings

The hash graph kernels effectively handle large graphs with continuous attributes.

Theoretical analysis confirms the scalability and correctness of the approach.

Experimental results demonstrate improved performance over existing methods.

Abstract

While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous attributes into discrete labels using randomized hash functions. We illustrate hash graph kernels for the Weisfeiler-Lehman subtree kernel and for the shortest-path kernel. The resulting novel graph kernels are shown to be, both, able to handle graphs with continuous attributes and scalable to large graphs and data sets. This is supported by our theoretical analysis and demonstrated by an extensive experimental evaluation.