Predicting City Poverty Using Satellite Imagery. - Simone Piaggesi; Laetitia Gauvin; Michele Tizzoni; Ciro Cattuto; Natalia Adler; Stefaan Verhulst; Andrew Young; Rhiannan Price; Leo Ferres; André Panisson (CVPR Workshops, 2019)

Gender gaps in urban mobility. - Laetitia Gauvin; Michele Tizzoni; Simone Piaggesi; Andrew Young; Natalia Adler; Stefaan Verhulst; Leo Ferres; Ciro Cattuto (CoRR, 2019)

Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis. - Francesco Bodria; André Panisson; Alan Perotti; Simone Piaggesi (SEBD, 2020)

Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex Embeddings. - Simone Piaggesi; André Panisson; Giovanni Petri (LoG, 2022)

Time-varying graph representation learning via higher-order skip-gram with negative sampling. - Simone Piaggesi; André Panisson (EPJ Data Sci., 2022)

Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data. - Simone Piaggesi; Serena Giurgola; Márton Karsai; Yelena Mejova; André Panisson; Michele Tizzoni (Frontiers Big Data, 2022)

Counterfactual and Prototypical Explanations for Tabular Data via Interpretable Latent Space. - Simone Piaggesi; Francesco Bodria; Riccardo Guidotti; Fosca Giannotti; Dino Pedreschi (IEEE Access, 2024)

Disentangled and Self-Explainable Node Representation Learning. - Simone Piaggesi; André Panisson; Megha Khosla (CoRR, 2024)

DINE: Dimensional Interpretability of Node Embeddings. - Simone Piaggesi; Megha Khosla; André Panisson; Avishek Anand (IEEE Trans. Knowl. Data Eng., 2024)

Explanations Go Linear: Interpretable and Individual Latent Encoding for Post-hoc Explainability. - Simone Piaggesi; Riccardo Guidotti; Fosca Giannotti; Dino Pedreschi (CoRR, 2025)