In this paper, we propose a scalable kernel for nodes in a (huge) graph. In contrast with other state-of-the-art kernels that scale more than quadratically in the number of nodes, our approach scales lin- early in the average out-degree and quadratically in the number of nodes (for the Gram matrix computation). The kernel presented in this paper considers neighbours as sets, thus it ignores edge weights. Nevertheless, experimental results on real-world datasets show promising results.
Approximated Neighbours MinHash Graph Node Kernel
Nicolò Navarin;Alessandro Sperduti
2017
Abstract
In this paper, we propose a scalable kernel for nodes in a (huge) graph. In contrast with other state-of-the-art kernels that scale more than quadratically in the number of nodes, our approach scales lin- early in the average out-degree and quadratically in the number of nodes (for the Gram matrix computation). The kernel presented in this paper considers neighbours as sets, thus it ignores edge weights. Nevertheless, experimental results on real-world datasets show promising results.
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