Pascal Welke

@pascalwelke.bsky.social

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Graph Machine Learning and Graph Mining Assistant Professor (Lecturer) in Data Science Lancaster University Leipzig https://pwelke.de

TL;DR More expressive GNNs outperform less expressive GNNs not due to expressivity. Pdf Version of the poster, as well as the paper is available at https://pwelke.de
Visual depiction of r-lGIN: During preprocessing, we calculate the path neighborhoods Nr (v) for each node v in the graph G. Paths of varying lengths are processed separately using simple GINs, and their embeddings are pooled to obtain the final graph embedding. The forward complexity scales linearly with the sizes of Nr (v), enabling efficient computation on sparse graphs.