33 citations · 42 across the 6 of their papers we have counts for
11 papers
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…
GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
Francesco Ferrini, Veronica Lachi, Antonio Longa +2
Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-centric message-passing schemes ove…
Bridging Theory and Practice in Link Representation with Graph Neural Networks
Veronica Lachi, Francesco Ferrini, Antonio Longa +3
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 expressi…
Boosting Relational Deep Learning with Pretrained Tabular Models
Veronica Lachi, Antonio Longa, Beatrice Bevilacqua +3
Relational databases, organized into tables connected by primary-foreign key relationships, are a common format for organizing data. Making predictions on relational data often inv…
The expressive power of pooling in Graph Neural Networks
Filippo Maria Bianchi, Veronica Lachi
In Graph Neural Networks (GNNs), hierarchical pooling operators generate local summaries of the data by coarsening the graph structure and the vertex features. While considerable a…
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…
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
Silvia Beddar-Wiesing, Giuseppe Alessio D'Inverno, Caterina Graziani +4
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On…