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pro vyhledávání: '"Schulz, Till"'
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often oversimplify the graph structu
Externí odkaz:
http://arxiv.org/abs/2406.03386
Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality of collected labeled fingerprin
Externí odkaz:
http://arxiv.org/abs/2207.02668
The majority of popular graph kernels is based on the concept of Haussler's $\mathcal{R}$-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtr
Externí odkaz:
http://arxiv.org/abs/2110.11862
The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance. Their key concept is based on an implicit comparison of neighborhood representing trees with respect to
Externí odkaz:
http://arxiv.org/abs/2101.08104
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:8196-8203
The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Usin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::525d113a6efca60f853d0d1d9e5044bb
https://doi.org/10.1609/aaai.v36i8.20793
https://doi.org/10.1609/aaai.v36i8.20793
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