82 citations · 307 across the 28 of their papers we have counts for
21 papers · 1 filter
Interpreting Temporal Graph Neural Networks with Koopman Theory
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Spatiotemporal graph neural networks (STGNNs) have shown promising results in many domains, from forecasting to epidemiology. However, understanding the dynamics learned by these m…
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting cons…
Probabilistic load forecasting with Reservoir Computing
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric pow…
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Indro Spinelli, Michele Guerra, Filippo Maria Bianchi +1
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection…
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…
Total Variation Graph Neural Networks
Jonas Berg Hansen, Filippo Maria Bianchi
Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation.…