833 citations · 1.7k across the 18 of their papers we have counts for
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cs.LG2023★ 4 cited
Fine-grained Expressivity of Graph Neural Networks
Jan Böker, Ron Levie, Ningyuan Huang +2
Numerous recent works have analyzed the expressive power of message-passing graph neural networks (MPNNs), primarily utilizing combinatorial techniques such as the -dimensional…
cs.LG2023★ 32 cited
Attending to Graph Transformers
Luis Müller, Mikhail Galkin, Christopher Morris +1
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks…
cs.LG2023
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…