33 citations · 42 across the 6 of their papers we have counts for
5 papers · 1 filter
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
Enrico Chialà, Francesco Ferrini, Antonio Longa +4
Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the dependenc…
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a use…
Rethinking Epistemic Uncertainty in Node Classification through Information Growth
Emma Meneghini, Francesco Ferrini, Bruno Lepri +2
Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, existing graph evidential deep lear…
Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment
Dionisia Naddeo, Jonas Linkerhägner, Nicola Toschi +2
Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of them. Based on this observation, H…
Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
Francesco Ferrini, Veronica Lachi, Antonio Longa +5
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 a…