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cs.LG2025
Attention Beyond Neighborhoods: Reviving Transformer for Graph Clustering
Xuanting Xie, Bingheng Li, Erlin Pan +3
Attention mechanisms have become a cornerstone in modern neural networks, driving breakthroughs across diverse domains. However, their application to graph structured data, where c…
cs.LG2025
Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization
Khushnood Abbas, Ruizhe Hou, Zhou Wengang +4
Graph Neural Networks (GNNs) became useful for learning on non-Euclidean data. However, their best performance depends on choosing the right model architecture and the training obj…