Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs
The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive power of message-passing architectures. Unfortunately, computing it presents two fundamental bottlenecks. First, classic algorithms are inherently sequential and cannot exploit modern massively parallel hardware. Second, these are \emph{global} algorithms, i.e., they require availability in memory of the full graph, severely limiting applicability to real-world instances. We leverage a linear-algebraic interpretation of 1-WL stable coloring and introduce two key contributions: (i)~a randomized refinement algorithm with tight probabilistic g...
| Authors | Filippo Biondi, Mirco Tribastone, Max Tschaikowski |
|---|---|
| Primary category | cs.DC |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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