3 papers
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.LG2025
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Guoming Li, Jian Yang, Yifan Chen
Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various gra…
cs.CL2025
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
P Team, Xinrun Du, Yifan Yao +94
Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…