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Mikhail Galkin

Alumni

Publications

Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampasek
Mikhail Galkin
Vijay Prakash Dwivedi
Anh Tuan Luu
Guy Wolf
Dominique Beaini
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art result… (voir plus)s on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net