2024
Even Sparser Graph Transformers
Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David Woodruff, Danica J. Sutherland
NeurIPS 2024poster
Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scaling to large graphs. Sparse attention variants such as Exphormer can help, but may require high-degree augmentations to t…