1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
WL meet VC
Christopher Morris, Floris Geerts, Jan Tönshoff +1
Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to the -dimensional Weisfeiler--Leman algorithm (). Here, t…
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
Jan Tönshoff, Berke Kisin, Jakob Lindner +1
We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can b…