Publications
Interpreting Temporal Graph Neural Networks with Koopman Theory
We propose an XAI technique based on Koopman theory to interpret temporal graphs and the spatio-temporal Graph Neural Newtworks used to process them. The proposed approach allows to identify nodes and time steps when relevant events occur.
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach is robust to the underlying graph topology and is fully differentiable, making it possible to find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, differentiable, and particularly suitable for downstream tasks on heterophilic graphs.
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
ICML 2024
Spatiotemporal graph neural networks achieve striking results by representing the relationships across time series as a graph. Nonetheless, most existing methods rely on the often unrealistic assumption that inputs are always available and fail to capture hidden spatiotemporal dynamics when part of the data is missing. In this work, we tackle this problem through hierarchical spatiotemporal downsampling. The input time series are progressively coarsened over time and space, obtaining a pool of representations that capture heterogeneous temporal and spatial dynamics. Conditioned on observations and missing data patterns, such representations are combined by an interpretable attention mechanism to generate the forecasts.