Faster Kernels for Graphs with Continuous Attributes via Hashing
This addresses a bottleneck for researchers and practitioners working with graph data that includes continuous attributes, offering a scalable solution, though it is incremental as it builds on existing discrete kernels.
The paper tackled the scalability issue of graph kernels for graphs with continuous attributes by introducing hash graph kernels, a framework that converts continuous attributes into discrete labels using randomized hash functions, resulting in kernels that handle continuous attributes and scale to large graphs, as shown in experiments.
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.