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cs.LG2026
Weisfeiler and Lehman Go Categorical
Seongjin Choi, Gahee Kim, Se-Young Yun
While lifting map has significantly enhanced the expressivity of graph neural networks, extending this paradigm to hypergraphs remains fragmented. To address this, we introduce the…
cs.LG2025
Federated Continual Recommendation
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +4
The increasing emphasis on privacy in recommendation systems has led to the adoption of Federated Learning (FL) as a privacy-preserving solution, enabling collaborative training wi…
cs.LG2025
Hypergraph Neural Sheaf Diffusion: A Symmetric Simplicial Set Framework for Higher-Order Learning
Seongjin Choi, Gahee Kim, Yong-Geun Oh
The absence of intrinsic adjacency relations and orientation systems in hypergraphs creates fundamental challenges for constructing sheaf Laplacians of arbitrary degrees. We resolv…