FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. - Giuseppe Attardi; Antonio Carta; Federico Errica; Andrea Madotto; Ludovica Pannitto (SemEval@ACL, 2017)

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. - Davide Bacciu; Federico Errica; Alessio Micheli (ICML, 2018)

Theoretically Expressive and Edge-aware Graph Learning. - Federico Errica; Davide Bacciu; Alessio Micheli (ESANN, 2020)

A Fair Comparison of Graph Neural Networks for Graph Classification. - Federico Errica; Marco Podda; Davide Bacciu; Alessio Micheli (ICLR, 2020)

Accelerating the identification of informative reduced representations of proteins with deep learning for graphs. - Federico Errica; Marco Giulini; Davide Bacciu; Roberto Menichetti; Alessio Micheli; Raffaello Potestio (CoRR, 2020)

Probabilistic Learning on Graphs via Contextual Architectures. - Davide Bacciu; Federico Errica; Alessio Micheli (J. Mach. Learn. Res., 2020)

A gentle introduction to deep learning for graphs. - Davide Bacciu; Federico Errica; Alessio Micheli; Marco Podda (Neural Networks, 2020)

Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. - Federico Errica; Giacomo Iadarola; Fabio Martinelli; Francesco Mercaldo; Alessio Micheli (ESANN, 2021)

Complex Data: Learning Trustworthily, Automatically, and with Guarantees. - Luca Oneto; Nicolò Navarin; Battista Biggio; Federico Errica; Alessio Micheli; Franco Scarselli; Monica Bianchini; Alessandro Sperduti (ESANN, 2021)

Graph Mixture Density Networks. - Federico Errica; Davide Bacciu; Alessio Micheli (ICML, 2021)

Modeling Edge Features with Deep Bayesian Graph Networks. - Daniele Atzeni; Davide Bacciu; Federico Errica; Alessio Micheli (IJCNN, 2021)

Concept Matching for Low-Resource Classification. - Federico Errica; Fabrizio Silvestri; Bora Edizel; Ludovic Denoyer; Fabio Petroni; Vassilis Plachouras; Sebastian Riedel 001 (IJCNN, 2021)

Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. - Antonio Carta; Andrea Cossu; Federico Errica; Davide Bacciu (CoRR, 2021)

Deep Learning for Graphs. - Davide Bacciu; Federico Errica; Nicolò Navarin; Luca Pasa; Daniele Zambon (ESANN, 2022)

The Infinite Contextual Graph Markov Model. - Daniele Castellana; Federico Errica; Davide Bacciu; Alessio Micheli (ICML, 2022)

Bayesian Deep Learning for Graphs. - Federico Errica (CoRR, 2022)

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. - Antonio Carta; Andrea Cossu; Federico Errica; Davide Bacciu (Frontiers Artif. Intell., 2022)

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. - Luca Oneto; Nicolò Navarin; Battista Biggio; Federico Errica; Alessio Micheli; Franco Scarselli; Monica Bianchini; Luca Demetrio; Pietro Bongini; Armando Tacchella; Alessandro Sperduti (Neurocomputing, 2022)

Bayesian Deep Learning for Graphs. - Federico Errica (2022)

Graph Representation Learning. - Davide Bacciu; Federico Errica; Alessio Micheli; Nicolò Navarin; Luca Pasa; Marco Podda; Daniele Zambon (ESANN, 2023)

Hidden Markov Models for Temporal Graph Representation Learning. - Federico Errica; Alessio Gravina; Davide Bacciu; Alessio Micheli (ESANN, 2023)

On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data. - Federico Errica (NeurIPS, 2023)

Investigating the Interplay between Features and Structures in Graph Learning. - Daniele Castellana; Federico Errica (CoRR, 2023)

Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. - Henrik Christiansen; Federico Errica; Francesco Alesiani (CoRR, 2023)

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Takashi Maruyama; Mathias Niepert; Francesco Alesiani (CoRR, 2023)

PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. - Federico Errica; Davide Bacciu; Alessio Micheli (Zenodo, 2023)

PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. - Federico Errica; Davide Bacciu; Alessio Micheli (J. Open Source Softw., 2023)

Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. - Federico Errica; Mathias Niepert (ICLR, 2024)

History Repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. - Julia Gastinger; Christian Meilicke; Federico Errica; Timo Sztyler; Anett Schülke; Heiner Stuckenschmidt (IJCAI, 2024)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Viktor Zaverkin; Francesco Alesiani; Takashi Maruyama; Federico Errica; Henrik Christiansen; Makoto Takamoto; Nicolas Weber; Mathias Niepert (NeurIPS, 2024)

Deep Graph Networks for Drug Repurposing With Multi-Protein Targets. - Davide Bacciu; Federico Errica; Alessio Gravina; Lorenzo Madeddu; Marco Podda; Giovanni Stilo (IEEE Trans. Emerg. Top. Comput., 2024)

What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering. - Federico Errica; Davide Sanvito; Giuseppe Siracusano; Roberto Bifulco (NAACL (Long Papers), 2025)

Adaptive Width Neural Networks. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Mathias Niepert; Francesco Alesiani (CoRR, 2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. - Henrik Christiansen; Takashi Maruyama; Federico Errica; Viktor Zaverkin; Makoto Takamoto; Francesco Alesiani (CoRR, 2025)

Oversmoothing, "Oversquashing", Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning. - Adrián Arnaiz-Rodríguez; Federico Errica (CoRR, 2025)

Variational Kolmogorov-Arnold Network. - Francesco Alesiani; Henrik Christiansen; Federico Errica (CoRR, 2025)