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cs.LG2026
The Expressive Power of Random-Feature MPNNs Across Graph Sizes
Robert R. Nerem, Mitchell Black, Antonis Vasileiou +2
Continuous random node features increase the expressivity of message-passing neural networks (MPNNs), but existing theoretical analyses are not fine-grained enough to capture their…
cs.LG2026
Understanding Truncated Positional Encodings for Graph Neural Networks
James Flora, Mitchell Black, Weng-Keen Wong +1
Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.g., Laplacia…