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Francesco Ferrini

2 accepted papers

2026

Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution

ICML 2026poster

Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly address relatively benign scenarios, namely benchmark datasets with (a) high-dimensional but sparse node features and (b)…

Cited by 0SourceScholar
2025

Bridging Theory and Practice in Link Representation with Graph Neural Networks

NeurIPS 2025spotlight

Graph Neural Networks (GNNs) are widely used to compute representations of node pairs for downstream tasks such as link prediction. Yet, theoretical understanding of their expressive power has focused almost entirely on graph-level representations. In this work, we shift the focus to links and provi…

Cited by 0SourceScholar