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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
Algorithmically Aligned Neural Agglomerative Tree Construction
Robert R Nerem, Pranav Singh, Cheyenne Ward +1
Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear which merge rule best suits a…
cs.LG2026
Which Algorithms Can Graph Neural Networks Learn?
Solveig Wittig, Antonis Vasileiou, Robert R. Nerem +4
In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a line of work often referred to as neural…