Abstract:Graph pooling is commonly applied in graph classification, yet its empirical gains over standard WL-1 expressive GNNs are often marginal or inconsistent. We study this gap by analysing the interaction between node features and graph topology and their effect on pooling objectives. Our analysis reveals that pooling operators require node features that are well-aligned with the graph's topology -- a condition often overlooked and not guaranteed in empirical networks. We formalise fundamental requirements for node features to enable effective pooling, and introduce a quantitative measure of feature quality. Our empirical evaluation shows that, when these requirements are satisfied, pooling can be beneficial and improve performance on appropriate datasets.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.06250 [cs.LG] |
| (or arXiv:2605.06250v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06250 arXiv-issued DOI via DataCite (pending registration) |
From: Christopher Blöcker [view email]
[v1]
Thu, 7 May 2026 13:27:48 UTC (257 KB)
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