1.6k citations · 2.4k across the 19 of their papers we have counts for
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cs.LG2022★ 49 cited
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Antonio Longa, Steve Azzolin, Gabriele Santin +4
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting…
cs.SI2022★ 2 cited
Generating fine-grained surrogate temporal networks
Antonio Longa, Giulia Cencetti, Sune Lehmann +2
Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world dat…