Publications

01

Conference

A Non-Aggregative Quantification Method for Graph Nodes

A. Micheli, A. Moreo, M. Podda, F. Sebastiani, D. Tortorella

In Accepted at ECML/PKDD Workshop on Quantification and Classification under Dataset Shift (QCDS 2026).

02

Conference

Network Quantification on Neuromorphic Hardware: Proof-of-concept with Randomized Ising Models

M. G. Berni, A. Micheli, M. Podda, D. Tortorella

In Accepted at ECML/PKDD Workshop on Deep Learning Meets Neuromorphic Hardware (DLMNH 2026).

03

Journal

A method for the systematic generation of graph XAI benchmarks via Weisfeiler–Leman coloring

M. Fontanesi, A. Micheli, M. Podda, D. Tortorella

Data Mining and Knowledge Discovery, 40(4), pp. 42.

Sjr 2025: q1

DOI

04

Journal

Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre

R. De Lucia, A. Micheli, M. Parollo, M. Podda, L. Pedrelli, F. Aliprandi, P. Pantelidis, L. Segreti, A. Di Cori, A. Parlato, A. Canu, G. Grifoni, V. Barletta, S. Viani, M. Giannotti Santoro, G. Zucchelli

Journal of Interventional Cardiac Electrophysiology.

Sjr 2025: q2

DOI

05

Journal

The added value of echocardiography in pulmonary arterial hypertension risk assessment. An artificial intelligence machine learning–derived analysis of the ULTRA RIGHT VALUE registry

K. Tello, P. Escribano-Subias, S. Ghio, M. Acquaro, D. A. Fegatelli, M. D’Alto, D. Filomena, A. Greco, G. Giannakoulas, F. L. Giudice, C. J. López-Guarch, G. Kopec, N. Kremer, A. Cruz-Utrilla, E. Maggio, G. Manzi, A. Mihai, A. Micheli, A. Orlando, S. Papa, V. Patsiou, M. Podda, W. Gin-Sing, G. Savonitto, L. Howard, P. Psochias, A. Nicoliello, L. Scelsi, J. Stepniewski, S. Yildiz, K. Jonas, A. Vergara, N. Cedrone, C. D. Vizza, R. Naeije, T. U. R. V. E. I. P. A. H. G. Badagliacca

European Heart Journal - Cardiovascular Imaging, pp. jeag232.

DOI

06

Conference

An Empirical Investigation of Shortcuts in Graph Learning

D. Tortorella, M. Fontanesi, A. Micheli, M. Podda

In Graph-Based Representations in Pattern Recognition, pp. 147–156. Springer Nature Switzerland.

DOI

07

Conference

Graph Diffusion that can Insert and Delete

M. Ninniri, M. Podda, D. Bacciu

In Advances in Neural Information Processing Systems, pp. 78375–78401. Curran Associates, Inc..

Core 2023: a*

08

Conference

Towards Efficient Molecular Property Optimization with Graph Energy Based Models

L. Miglior, L. Simone, M. Podda, D. Bacciu

In Proceedings of the 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 289–294. i6doc.com.

Core 2023: b

DOI

09

Conference

Analyzing Explanations of Deep Graph Networks through Node Centrality and Connectivity

M. Fontanesi, A. Micheli, M. Podda, D. Tortorella

In Discovery Science, pp. 295–309. Springer Nature Switzerland.

Core 2023: b

DOI

010

Conference

Relating Explanations with the Inductive Biases of Deep Graph Networks

M. Fontanesi, A. Micheli, M. Podda

In AIxIA 2024 - Advances in Artificial Intelligence, pp. 175–187. Springer Nature Switzerland.

DOI

011

Conference

Investigating Time-Scales in Deep Echo State Networks for Natural Language Processing

C. Baccheschi, A. Bondielli, A. Lenci, A. Micheli, L. Passaro, M. Podda, D. Tortorella

In Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions, pp. 188–200. Springer Nature Switzerland.

DOI

012

Journal

A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens

M. Podda, C. Savojardo, P. L. Martelli, R. Casadio, A. Sîrbu, C. Priami, A. Brozzi

PLOS ONE, 20(6), pp. e0323895.

Sjr 2025: q1

DOI

013

Journal

Efficient Quantification on Large-Scale Networks

A. Micheli, A. Moreo, M. Podda, F. Sebastiani, W. Simoni, D. Tortorella

Machine Learning, 114(12), pp. 270.

Sjr 2025: q1

DOI

014

Journal

Misinformation mitigation in online social networks using continual learning with graph neural networks

H. Merini, A. I. E. Hosni, K. Beghdad Bey, V. Lomonaco, M. Podda, I. Baira

Online Social Networks and Media, 50, pp. 100340.

Sjr 2025: q1

DOI

015

Journal

Bridging XAI and spectral analysis to investigate the inductive biases of deep graph networks

M. Fontanesi, A. Micheli, M. Podda, D. Tortorella

Machine Learning, 114(11), pp. 257.

Sjr 2025: q1

DOI

016

Journal

Sensitivity analysis on Protein-Protein Interaction Networks through Deep Graph Networks

A. Dipalma, M. Fontanesi, A. Micheli, P. Milazzo, M. Podda

BMC Bioinformatics, 26(1), pp. 124.

Sjr 2025: q1

DOI

017

Conference

How Much Do DNA and Protein Deep Embeddings Preserve Biological Information?

M. Tolloso, S. G. Galfrè, A. Pavone, M. Podda, A. Sîrbu, C. Priami

In Computational Methods in Systems Biology, pp. 209–225. Springer Nature Switzerland.

DOI

018

Conference

Classifier-free graph diffusion for molecular property targeting

M. Ninniri, M. Podda, D. Bacciu

In 4th Workshop on Graphs and More Complex Structures for Learning and Reasoning, co-located with AAAI 2024.

019

Conference

Classifier-Free Graph Diffusion for Molecular Property Targeting

M. Ninniri, M. Podda, D. Bacciu

In Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD, pp. 318–335. Springer Nature Switzerland.

Core 2023: a

DOI

020

Conference

XAI and Bias of Deep Graph Networks

M. Fontanesi, A. Micheli, M. Podda

In Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 41–46. i6doc.com.

Core 2023: b

DOI

021

Conference

Explaining Graph Classifiers by Unsupervised Node Relevance Attribution

M. Fontanesi, A. Micheli, M. Podda

In Explainable Artificial Intelligence, pp. 63–74. Springer Nature Switzerland.

DOI

022

Journal

Deep Graph Networks for Drug Repurposing with Multi-Protein Targets

D. Bacciu, F. Errica, A. Gravina, L. Madeddu, M. Podda, G. Stilo

IEEE Transactions on Emerging Topics in Computing, 12(1), pp. 177–189.

Sjr 2024: q1

DOI

023

Journal

Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning

M. Podda, S. Bonechi, A. Palladino, M. Scaramuzzino, A. Brozzi, G. Roma, A. Muzzi, C. Priami, A. Sîrbu, M. Bodini

iScience, 27(3), pp. 109257.

Sjr 2024: q1

DOI

024

Conference

Graph Representation Learning

D. Bacciu, F. Errica, A. Micheli, N. Navarin, L. Pasa, M. Podda, D. Zambon

In Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 1–10. i6doc.com.

Core 2023: b

DOI

025

Journal

Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs

M. Fontanesi, A. Micheli, P. Milazzo, M. Podda

Bioinformatics, 39(11), pp. btad678.

Sjr 2023: q1

DOI

026

Book Chapter

Deep Learning in Cheminformatics

A. Micheli, M. Podda

In Deep Learning in Biology and Medicine, pp. 157–195. World Scientific Publishing.

DOI

027

Conference

A rigorous evaluation of embeddings-based vs. feature-based machine learning models for protein antigenicity prediction

M. Podda, A. Sîrbu, C. Priami, A. Brozzi

In 10th Italian Workshop on Machine Learning and Data Mining, part of AIxIA 2021.

028

Conference

Classification of Biochemical Pathway Robustness with Neural Networks for Graphs

M. Podda, P. Bove, A. Micheli, P. Milazzo

In Communications in Computer and Information Science, pp. 215–239. Springer International Publishing.

DOI

029

Conference

GraphGen-Redux: A Fast and Lightweight Recurrent Model for Labeled Graph Generation

D. Bacciu, M. Podda

In International Joint Conference on Neural Networks, pp. 1–8. IEEE.

Core 2021: b

DOI

030

Journal

In silico modeling of biochemical pathways

P. Milazzo, R. Gori, A. Micheli, L. Nasti, M. Podda

Biomedical Science and Engineering, 4(s1).

DOI

031

Conference

Biochemical Pathway Robustness Prediction with Graph Neural Networks

M. Podda, D. Bacciu, A. Micheli, P. Milazzo

In Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 121–126. i6doc.com.

Core 2020: b

032

Conference

A Deep Generative Model for Fragment-Based Molecule Generation

M. Podda, D. Bacciu, A. Micheli

In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, pp. 2240–2250. PMLR.

Core 2020: a

033

Conference

A Fair Comparison of Graph Neural Networks for Graph Classification

F. Errica, M. Podda, D. Bacciu, A. Micheli

In 8th International Conference on Learning Representations.

Core 2023: a*

034

Conference

Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks

P. Bove, A. Micheli, P. Milazzo, M. Podda

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, pp. 32–43. SCITEPRESS.

DOI

035

Journal

Edge-based sequential graph generation with recurrent neural networks

D. Bacciu, A. Micheli, M. Podda

Neurocomputing, 416, pp. 177–189.

Sjr 2020: q1

DOI

036

Journal

A gentle introduction to deep learning for graphs

D. Bacciu, F. Errica, A. Micheli, M. Podda

Neural Networks, 129, pp. 203–221.

Sjr 2020: q1

DOI

037

Conference

Preliminary Results on Predicting Robustness of Biochemical Pathways through Machine Learning on Graphs

P. Bove, A. Micheli, P. Milazzo, M. Podda

In Pre-proceedings of the 8th International Symposium From Data to Models and Back (DataMod).

038

Conference

Graph generation by sequential edge prediction

D. Bacciu, A. Micheli, M. Podda

In Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 95–100. i6doc.com.

Core 2018: b

039

Journal

A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor

M. Podda, D. Bacciu, A. Micheli, R. Bellù, G. Placidi, L. Gagliardi

Scientific Reports, 8(1), pp. 13743.

Sjr 2018: q1

DOI

Publications

2026

A Non-Aggregative Quantification Method for Graph Nodes

Network Quantification on Neuromorphic Hardware: Proof-of-concept with Randomized Ising Models

A method for the systematic generation of graph XAI benchmarks via Weisfeiler–Leman coloring

Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre

The added value of echocardiography in pulmonary arterial hypertension risk assessment. An artificial intelligence machine learning–derived analysis of the ULTRA RIGHT VALUE registry

2025

An Empirical Investigation of Shortcuts in Graph Learning

Graph Diffusion that can Insert and Delete

Towards Efficient Molecular Property Optimization with Graph Energy Based Models

Analyzing Explanations of Deep Graph Networks through Node Centrality and Connectivity

Relating Explanations with the Inductive Biases of Deep Graph Networks

Investigating Time-Scales in Deep Echo State Networks for Natural Language Processing

A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens

Efficient Quantification on Large-Scale Networks

Misinformation mitigation in online social networks using continual learning with graph neural networks

Bridging XAI and spectral analysis to investigate the inductive biases of deep graph networks

Sensitivity analysis on Protein-Protein Interaction Networks through Deep Graph Networks

2024

How Much Do DNA and Protein Deep Embeddings Preserve Biological Information?

Classifier-free graph diffusion for molecular property targeting

Classifier-Free Graph Diffusion for Molecular Property Targeting

XAI and Bias of Deep Graph Networks

Explaining Graph Classifiers by Unsupervised Node Relevance Attribution

Deep Graph Networks for Drug Repurposing with Multi-Protein Targets

Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning

2023

Graph Representation Learning

Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs

2022

Deep Learning in Cheminformatics

2021

A rigorous evaluation of embeddings-based vs. feature-based machine learning models for protein antigenicity prediction

Classification of Biochemical Pathway Robustness with Neural Networks for Graphs

GraphGen-Redux: A Fast and Lightweight Recurrent Model for Labeled Graph Generation

In silico modeling of biochemical pathways

2020

Biochemical Pathway Robustness Prediction with Graph Neural Networks

A Deep Generative Model for Fragment-Based Molecule Generation

A Fair Comparison of Graph Neural Networks for Graph Classification

Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks

Edge-based sequential graph generation with recurrent neural networks

A gentle introduction to deep learning for graphs

2019

Preliminary Results on Predicting Robustness of Biochemical Pathways through Machine Learning on Graphs

Graph generation by sequential edge prediction

2018

A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor