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cs.LG2025
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
cs.LG2024
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
Eran Rosenbluth, Jan Tönshoff, Martin Ritzert +2
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal func…
cs.LG2023
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth +1
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical…