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cs.LG2025
Convexified Message-Passing Graph Neural Networks
Saar Cohen, Noa Agmon, Uri Shaham
Graph Neural Networks (GNNs) are key tools for graph representation learning, demonstrating strong results across diverse prediction tasks. In this paper, we present Convexified Me…
cs.LG2024
SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning
Yahel Jacobs, Reut Dayan, Uri Shaham
Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections…