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cs.LG2026
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
Guoming Li, Jian Yang, Xukun Wang +3
Coarsening-based training for graph neural networks (GNNs), i.e.\ training on coarsened graphs rather than the original large ones, has become a promising direction for scaling GNN…
cs.LG2026
Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
Guoming Li, Shangyu Zhang, Junwei Pan +7
Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation a…