Publications

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

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

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Henrik Christiansen, Federico Errica, Francesco Alesiani (2023). Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. Journal of Chemical Physics.

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Federico Errica, Davide Bacciu, Alessio Micheli (2023). PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. Journal of Open Source Software.

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

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Daniele Castellana, Federico Errica (2023). Investigating the Interplay between Features and Structures in Graph Learning. MLG Workshop at ECMLPKDD 2023 Benchmarks.

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

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Luca Oneto, Nicolo Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti (2022). Towards Learning Trustworthily, Automatically, and with Guarantees on Graphs: an Overview. In Neurocomputing.

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Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu (2022). Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. In Frontiers in Artificial Intelligence.

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

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

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

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

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Federico Errica, Marco Giulini, Davide Bacciu, Roberto Menichetti, Alessio Micheli, Raffaello Potestio (2021). A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins. In Frontiers Molecular Biosciences.

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Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu (2021). Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. The Web Conference 2021, Workshop on Graph Learning Benchmarks.

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

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

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Davide Bacciu, Federico Errica, Alessio Micheli (2020). Probabilistic Learning on Graphs via Contextual Architectures. In JMLR.

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Davide Bacciu, Federico Errica, Marco Podda, Alessio Micheli (2020). A Gentle Introduction to Deep Learning for Graphs. In Neural Networks.

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

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

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