Domenico Tortorella, PhD

Domenico Tortorella, PhD

Assistant professor (RTD-A)

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks

Published in Pattern Recognition Letters, 2025

We propse an evaluation framework for graph rewiring methods based on training-free GNNs. In our fair conditions, rewiring rarely improves message-passing on 12 node and graph classification tasks.

Recommended citation: A. Micheli, D. Tortorella (2025). "An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks." Pattern Recognition Letters, vol. 196, pp. 134–141.
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Designs of Graph Echo State Networks for Node Classification

Published in Neurocomputing, 2024

Analysis of dense and sparse reservoir designs for node-level GESN via topology-dependent and topology-agnostic richness measures for node embeddings.

Recommended citation: A. Micheli, D. Tortorella (2024). "Designs of Graph Echo State Networks for Node Classification." Neurocomputing, vol. 597, 127965.
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Discrete-Time Dynamic Graph Echo State Networks

Published in Neurocomputing, 2022

DynGESN is introduced as a novel reservoir computing model for temporal graphs. More efficient then temporal graph kernels and 100x faster than temporal GNNs.

Recommended citation: A. Micheli, D. Tortorella (2022). "Discrete-Time Dynamic Graph Echo State Networks." Neurocomputing, vol. 496, pp. 85-95.
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Conference Papers

An Empirical Investigation of Shortcuts in Graph Learning

Published in GbRPR, 2025

We investigate shortcut learning in graphs by experimenting with adding training counterfactuals and shifting inductive bias.

Recommended citation: D. Tortorella, M. Fontanesi, A. Micheli, M. Podda (2025). "An Empirical Investigation of Shortcuts in Graph Learning." Graph-Based Representations in Pattern Recognition. GbRPR 2025. LNCS vol. 15727, pp. 147-156.
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Robustness in Protein-Protein Interaction Networks: A Link Prediction Approach

Published in ESANN, 2025

Reframing the prediction of dynamical properties of biochemical pathways as a link prediction task over the protein-protein interaction networks (PPINs).

Recommended citation: A. Dipalma, D. Tortorella, A. Micheli (2025). "Robustness in Protein-Protein Interaction Networks: A Link Prediction Approach." Proceedings of the 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2025), pp. 277-282.
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Encoding Graph Topology with Randomized Ising Models

Published in ESANN, 2025

We propose a RIM, a new model for encoding graph topology that is ameanable to physical implementation on non-conventional hardware.

Recommended citation: D. Tortorella, A. Brau, A. Micheli (2025). "Encoding Graph Topology with Randomized Ising Models." Proceedings of the 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2025), pp. 271-276.
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Analyzing Explanations of Deep Graph Networks through Node Centrality and Connectivity

Published in Discovery Science, 2024

We analyze the alignment of DGNs explanations to node centrality and graph connectivity, highlighting the presence of different inductive biases.

Recommended citation: M. Fontanesi, A. Micheli, M. Podda, D. Tortorella (2025). "Analyzing Explanations of Deep Graph Networks through Node Centrality and Connectivity." Proceedings of the 27th International Conference on Discovery Science (DS 2024), LNCS vol. 15243, pp. 295-309.
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Continual Learning with Graph Reservoirs: Preliminary experiments in graph classification

Published in ESANN, 2024

GESN relieves part of catastrophic forgetting in the continual learning setting by avoiding training representations for graph classification.

Recommended citation: D. Tortorella, A. Micheli (2024). "Continual Learning with Graph Reservoirs: Preliminary experiments in graph classification." Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2024), pp. 35-40.
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Onion Echo State Networks: A Preliminary Analysis of Dynamics

Published in ICANN, 2024

A preliminary analysis of dynamical properties of Onion ESN, a novel reservoir with groups of units presentig an annular spectrum.

Recommended citation: D. Tortorella, A. Micheli (2024). "Onion Echo State Networks: A Preliminary Analysis of Dynamics." Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), LNCS vol. 15025, pp. 117-128.
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Continuously Deep Recurrent Neural Networks

Published in ECML PKDD, 2024

A continuous-depth ESN is proposed, where a smooth depth hyperparameter regulates the extent of local connections.

Recommended citation: A. Ceni, P. F. Dominey, C. Gallicchio, A. Micheli, L. Pedrelli, D. Tortorella (2024). "Continuously Deep Recurrent Neural Networks." Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2024. LNCS vol. 14947, pp. 59-73.
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Entropy Based Regularization Improves Performance in the Forward-Forward Algorithm

Published in ESANN, 2023

Adding a representation entropy term into the loss of Hinton’s FFA improves accuracy.

Recommended citation: M. Pardi, D. Tortorella, A. Micheli (2023). "Entropy Based Regularization Improves Performance in the Forward-Forward Algorithm." Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 393-398.
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Richness of Node Embeddings in Graph Echo State Networks

Published in ESANN, 2023

Preliminary analysis of GESN’s node embedding richness via entropy and numerical analysis metrics.

Recommended citation: D. Tortorella, A. Micheli (2023). "Richness of Node Embeddings in Graph Echo State Networks." Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 11-16.
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Minimum Spanning Set Selection in Graph Kernels

Published in GbRPR, 2023

Minimizing the number of support vectors in SVM without any loss of accuracy via an RRQR factorization of kernel matrix.

Recommended citation: D. Tortorella, A. Micheli (2023). "Minimum Spanning Set Selection in Graph Kernels." Graph-Based Representations in Pattern Recognition. GbRPR 2023. LNCS vol. 14121, pp. 15-24.
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Leave Graphs Alone: Addressing Over-Squashing without Rewiring

Published in LoG, 2022

GESN achieves a significantly better accuracy on six heterophilic node classification tasks via tuning Lipschitz constants instead of resorting to graph rewiring.

Recommended citation: D. Tortorella, A. Micheli (2022). "Leave Graphs Alone: Addressing Over-Squashing without Rewiring" (Extended Abstract). Presented at the First Learning on Graphs Conference (LoG 2022), Virtual Event, December 9–12, 2022.
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Beyond Homophily with Graph Echo State Networks

Published in ESANN, 2022

Preliminary experiments on heterophilic node classification with GESN, showing the effectiveness of going beyond stability constraints.

Recommended citation: D. Tortorella, A. Micheli (2022). "Beyond Homophily with Graph Echo State Networks." Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022), pp. 491-496.
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Hierarchical Dynamics in Deep Echo State Networks

Published in ICANN, 2022

An in-depth theoretical analysis of asymptotic dynamics in Deep ESNs with different contractivity hierarchies.

Recommended citation: D. Tortorella, C. Gallicchio, A. Micheli (2022). "Hierarchical Dynamics in Deep Echo State Networks." Proceedings of the 31st International Conference on Artificial Neural Networks (ICANN 2022), pp. 668-679.
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Spectral Bounds for Graph Echo State Network Stability

Published in IJCNN, 2022

More accurate stability bounds for GESN based on graph spectral properties.

Recommended citation: D. Tortorella, C. Gallicchio, A. Micheli (2022). "Spectral Bounds for Graph Echo State Network Stability." Proceedings of the 2022 International Joint Conference on Neural Networks.
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Dynamic Graph Echo State Networks

Published in ESANN, 2021

Preliminary experiments on temporal graph classification with DynGESN, a novel reservoir computing model for dynamic graphs.

Recommended citation: D. Tortorella, A. Micheli (2021). "Dynamic Graph Echo State Networks." Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), pp. 99-104.
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