no code implementations • 22 Jun 2022 • Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.
1 code implementation • 25 Mar 2022 • Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh
While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs.
no code implementations • 4 Mar 2021 • Gaurav Rattan, Tim Seppelt
Cospectrality yields an equivalence relation on the family of graphs which is provably weaker than isomorphism.
Data Structures and Algorithms Discrete Mathematics Combinatorics 03B70, 05C81, 05C50, 05C85, 15A18, 15A24, 15A69 (Primary) 68R05, 68R10 (Secondary) F.4; G.2.2
1 code implementation • NeurIPS 2020 • Christopher Morris, Gaurav Rattan, Petra Mutzel
Hence, it accounts for the higher-order interactions between vertices.
Ranked #3 on Graph Classification on NCI109
1 code implementation • 4 Oct 2018 • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #4 on Graph Classification on NCI1