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arXiv · 1806.11377

Learning from graphs with structural variation

Rune Kok Nielsen·Andreas Nugaard Holm·Aasa Feragen
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Abstract

We study the effect of structural variation in graph data on the predictive performance of graph kernels. To this end, we introduce a novel, noise-robust adaptation of the GraphHopper kernel and validate it on benchmark data, obtaining modestly improved predictive performance on a range of datasets. Next, we investigate the performance of the state-of-the-art Weisfeiler-Lehman graph kernel under increasing synthetic structural errors and find that the effect of introducing errors depends strongly on the dataset.

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Rune Kok Nielsen, Andreas Nugaard Holm, Aasa Feragen. 2018-06-29. Learning from graphs with structural variation. https://arxiv.org/abs/1806.11377

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