Published in Machine Learning, 2025
We propose XNQ, an efficient model for network quantification that scales on large networks and is resilient to heterophily.
Recommended citation: A. Micheli, A. Moreo, M. Podda, F. Sebastiani, W. Simoni, D. Tortorella (2025). "Efficient quantification on large-scale networks." Machine Learning, vol. 114, 270.
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Published in Machine Learning, 2025
We apply graph signal processing to node importances of XAI explanations to refine the characterization of DGN inductive biases.
Recommended citation: M. Fontanesi, A. Micheli, M. Podda, D. Tortorella (2025). "Bridging XAI and spectral analysis to investigate the inductive biases of deep graph networks." Machine Learning, vol. 114, 257.
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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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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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Published in Neurocomputing, 2023
Node-level GESN is highly effective for heterophilic node classification tasks, while also being efficient and resilient to over-smoothing.
Recommended citation: A. Micheli, D. Tortorella (2023). "Addressing Heterophily in Node Classification with Graph Echo State Networks." Neurocomputing, vol. 550, 126506.
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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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Published in ICANN, 2025
An analysis of time-scales in Deep Bidirectional ESNs applied to sentence-level and token-level NLP tasks.
Recommended citation: C. Baccheschi, A. Bondielli, A. Lenci, A. Micheli, L. Passaro, M. Podda, D. Tortorella (2025). "Investigating Time-Scales in Deep Echo State Networks for Natural Language Processing." Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions, LNCS vol. 16072, pp. 188-200.
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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