7 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 7 cited
Expressiveness and Approximation Properties of Graph Neural Networks
Floris Geerts, Juan L. Reutter
Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are,…
cs.CC2020
Expressive power of linear algebra query languages
Floris Geerts, Thomas Muñoz, Cristian Riveros +1
Linear algebra algorithms often require some sort of iteration or recursion as is illustrated by standard algorithms for Gaussian elimination, matrix inversion, and transitive clos…
cs.LG2020★ 5 cited
The expressive power of kth-order invariant graph networks
Floris Geerts
The expressive power of graph neural network formalisms is commonly measured by their ability to distinguish graphs. For many formalisms, the k-dimensional Weisfeiler-Leman (k-WL)…