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A unified layer equation for GNNs reveals how over 200 architectures can be systematically compared and optimized for performance.
TopoFormer achieves state-of-the-art performance in graph learning by seamlessly integrating topological structures into attention-based models, outperforming traditional methods.
Transfer from node classification to link prediction is consistently advantageous, but the reverse can degrade performance unless structural conditions are ideal.
