833 citations · 1.7k across the 18 of their papers we have counts for
22 papers
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…
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…
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…
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris +1
Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emer…
Ordered Subgraph Aggregation Networks
Chendi Qian, Gaurav Rattan, Floris Geerts +2
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs. However, there is a limite…
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Elias B. Khalil, Christopher Morris, Andrea Lodi
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solve…