The paper extends the Strong Expressive Lottery Ticket Hypothesis to relational and temporal graph neural networks by proving that sufficiently large RGNNs contain sparse subnetworks preserving 1‑relational Weisfeiler‑Leman expressivity. It derives a probabilistic lower bound for random pruning to achieve such subnetworks and shows that common TGNNs and cross‑graph message passing can be reformulated as RGNNs to inherit these guarantees. Experiments validate the bound, compare it to empirical probabilities on synthetic data, and explore the relationship between pre‑training expressivity, optimization behavior, and prediction quality on temporal and molecular benchmarks.
By Lorenz Kummer, Samir Moustafa, Anatol Ehrlich, Franka Bause, Marco Nennstiel, Przemys{\l}aw Andrzej Wa{\l}\c{e}ga, Nils Morten Kriege
arXiv:2602. 14239v3 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science.
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arXiv:2606. 08491v1 Announce Type: new Abstract: Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning.
By Yao Cheng, Siqiang Luo
arXiv:2602.13106v2 Announce Type: replace-cross
Abstract: In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a li...
By Solveig Wittig, Antonis Vasileiou, Robert R. Nerem, Timo Stoll, Floris Geerts, Yusu Wang, Christopher Morris
arXiv:2602. 01553v3 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies.
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arXiv:2607. 20555v1 Announce Type: new Abstract: The lottery ticket hypothesis proposes that large random neural networks contain sparse subnetworks that can match the performance of dense models after comparable training.
By Bryce A. Christopherson, Jack Baretz, Darian Colgrove, Salah Dandan