A Unifying Relational Perspective on Expressive Lottery Tickets
Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Strong Expressive Lottery Ticket Hypothesis (SELTH) posits the existence of sparse GNNs that preserve Weisfeiler-Leman (WL) expressivity on static graphs. We generalize this existence result to a probabilistic statement for multi-relational and temporal domains via the relational WL (RWL). We prove that sufficiently parameterized RGNNs contain sparse subnetworks that maintain 1-RWL expressivity and derive a lower bound on the probability that a random pruning yields such a subnetwork. We show that common TGNNs and c...
| Authors | Lorenz Kummer, Samir Moustafa, Anatol Ehrlich et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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