10 citations · 26 across the 21 of their papers we have counts for
3 papers · 2 filters
Cooperative Graph Neural Networks
Ben Finkelshtein, Xingyue Huang, Michael Bronstein +1
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant t…
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Xingyue Huang, Miguel Romero Orth, İsmail İlkan Ceylan +1
Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for si…
Zero-One Laws of Graph Neural Networks
Sam Adam-Day, Theodor Mihai Iliant, İsmail İlkan Ceylan
Graph neural networks (GNNs) are the de facto standard deep learning architectures for machine learning on graphs. This has led to a large body of work analyzing the capabilities a…