89 citations · 113 across the 11 of their papers we have counts for
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cs.LG2020
Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, Pascal Frossard
Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning lo…
cs.LG2020
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Nikolaos Karalias, Andreas Loukas
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning fra…
cs.LG2020
How hard is to distinguish graphs with graph neural networks?
Andreas Loukas
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs. This study derives hardness results for the classification variant of gra…