1 citations · 1 across the 10 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
Almost Surely Asymptotically Constant Graph Neural Networks
Sam Adam-Day, Michael Benedikt, İsmail İlkan Ceylan +1
We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of real-valued GNN classifiers, such as those classifying graphs prob…
cs.LG2023
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…