Faster Kernels for Graphs with Continuous Attributes via HashingChristopher Morris; Nils M. Kriege; Kristian Kersting; Petra Mutzel
In: Francesco Bonchi; Josep Domingo-Ferrer; Ricardo Baeza-Yates; Zhi-Hua Zhou; Xindong Wu (Hrsg.). IEEE 16th International Conference on Data Mining. IEEE International Conference on Data Mining (ICDM-2016), December 12-15, Barcelona, Spain, Pages 1095-1100, IEEE Computer Society, 2016.
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.