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pro vyhledávání: '"graph kernels"'
Graph Neural Networks (GNNs) have emerged as a dominant approach in graph representation learning, yet they often struggle to capture consistent similarity relationships among graphs. While graph kernel methods such as the Weisfeiler-Lehman subtree (
Externí odkaz:
http://arxiv.org/abs/2410.23748
We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels (RWKs) for sparse graphs. This includes both labelled and unlabelled instances. The previous fastest
Externí odkaz:
http://arxiv.org/abs/2410.10368
Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling of complex objects, each of which can be annotated by metadata. Nonetheless, seemingly simple questions, such as dete
Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution. Utilizing representation learning has been shown as a promising direction to represent substructures and approximate the solution. Grap
Externí odkaz:
http://arxiv.org/abs/2405.07497
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
Externí odkaz:
http://arxiv.org/abs/2402.03838
Akademický článek
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Autor:
Choromanski, Krzysztof
We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be used to construct unbiased randomized estimators of several important kernels defined on graphs' nodes, in particular the regularized Laplacian kernel. As regular R
Externí odkaz:
http://arxiv.org/abs/2305.00156
Akademický článek
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Autor:
Young, James
The purpose of this review is to introduce the reader to graph kernels and the corresponding literature, with an emphasis on those with direct application to chemoinformatics. Graph kernels are functions that allow for the inference of properties of
Externí odkaz:
http://arxiv.org/abs/2208.04929