833 citations · 1.7k across the 18 of their papers we have counts for
5 papers · 1 filter
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…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
Christopher Morris, Gaurav Rattan, Sandra Kiefer +1
While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, hig…