49 citations · 82 across the 4 of their papers we have counts for
4 papers
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Li Sun, Zhenhao Huang, Yiding Wang +3
Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain ric…
xAI-Drop: Don't Use What You Cannot Explain
Vincenzo Marco De Luca, Antonio Longa, Pietro Liò +1
Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to…
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin +5
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the grap…
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…