15 citations · 24 across the 7 of their papers we have counts for
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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.LG2021★ 15 cited
Graph Neural Networks with Local Graph Parameters
Pablo Barceló, Floris Geerts, Juan Reutter +1
Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between -tuples of vertices. The distinguishing power of these "…