May 27, 2024, 4:45 a.m. | Isaac Reid, Krzysztof Choromanski, Eli Berger, Adrian Weller

cs.LG updates on

arXiv:2310.04859v3 Announce Type: replace-cross
Abstract: We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This includes many of the most popular examples of kernels defined on the nodes of a graph. Our algorithm enjoys subquadratic time complexity with respect to the number of nodes, overcoming the notoriously prohibitive cubic scaling of exact graph kernel evaluation. It can also be trivially distributed across machines, permitting learning …

abstract algorithm arxiv complexity cs.lg examples features functions general graph matrix nodes novel popular random replace type unbiased universal

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