Daniele Zambon · Daniele's publications

Selected news!

[4-2024] New paper on irregularly-sampled time series accepted at IJCAI ‘24!
[1-2024] New paper on virtual sensing accepted at ICLR ‘24!
[12-2023] We will be holding a special session on deep learning for graphs at IEEE WCCI 2024. Deadline for paper submission is January 15, 2024. Don’t miss out!
[9-2023] New paper putting together our knowledge on designing graph DL models for time series forecasting.
[9-2023] Our study on modeling local effects in STGNNs has been accepted at NeurIPS ‘23.
[8-2023] Our paper on graph learning from time series will be published in JMLR!
[7-2023] Working on a survey on GNN architectures for time series analysis: check out our preprint GNN4TS.
[3-2023] A bunch of preprints about Kalman filters [1], spatio-temporal models [2, 3, 4], and model optimality [5].

List of publications

Preprints

Graph Deep Learning for Time Series Forecasting, 2023, Cini, Marisca, Zambon, Alippi.

[ arXiv]
[ bibtex]

@misc{cini2023graph,
  title = {Graph {{Deep Learning}} for {{Time Series Forecasting}}},
  author = {Cini, Andrea and Marisca, Ivan and Zambon, Daniele and Alippi, Cesare},
  year = {2023},
  month = oct,
  number = {arXiv:2310.15978},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2310.15978},
  archiveprefix = {arxiv}
}

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, 2023, Jin, Koh, Wen, Zambon, Alippi, Webb, King, Pan.

[ arXiv]
[ GitHub]
[ bibtex]
[ video]

@misc{jin2023survey,
   title = {A {{Survey}} on {{Graph Neural Networks}} for {{Time Series}}: {{Forecasting}}, {{Classification}}, {{Imputation}}, and {{Anomaly Detection}}},
   shorttitle = {A {{Survey}} on {{Graph Neural Networks}} for {{Time Series}}},
   author = {Jin, Ming and Koh, Huan Yee and Wen, Qingsong and Zambon, Daniele and Alippi, Cesare and Webb, Geoffrey I. and King, Irwin and Pan, Shirui},
   year = {2023},
   month = jul,
   number = {arXiv:2307.03759},
   primaryclass = {cs},
   publisher = {arXiv},
   doi = {10.48550/arXiv.2307.03759},
   archiveprefix = {arxiv}
}

Graph Kalman Filters, 2023, Alippi*, Zambon*.

[ arXiv]
[ bibtex]
[ video]

@misc{alippi2023graph,
  title = {Graph {{Kalman Filters}}},
  author = {Alippi, Cesare and Zambon, Daniele},
  year = {2023},
  month = mar,
  number = {arXiv:2303.12021},
  primaryclass = {cs, stat},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2303.12021},
  archiveprefix = {arxiv}
}

Where and How to Improve Graph-based Spatio-Temporal Predictors, 2023, Zambon, Alippi.

[ arXiv]
[ bibtex]

@misc{zambon2023where,
  title = {Where and {{How}} to {{Improve Graph-based Spatio-temporal Predictors}}},
  author = {Zambon, Daniele and Alippi, Cesare},
  year = {2023},
  month = feb,
  number = {arXiv:2302.01701},
  primaryclass = {cs, stat},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2302.01701},
  archiveprefix = {arxiv}
}

Graph state-space models, 2023, Zambon, Cini, Livi, Alippi.

[ arXiv]
[ bibtex]

@misc{zambon2023graph,
  title = {Graph State-Space Models},
  author = {Zambon, Daniele and Cini, Andrea and Livi, Lorenzo and Alippi, Cesare},
  year = {2023},
  month = jan,
  number = {arXiv:2301.01741},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2301.01741},
  archiveprefix = {arxiv}
}

Journals

Sparse Graph Learning for Spatiotemporal Time Series, JMLR, 2023, Cini, Zambon, Alippi.

[ arXiv]
[ GitHub]
[ bibtex]

@article{cini2023sparse,
   title = {Sparse {{Graph Learning}} from {{Spatiotemporal Time Series}}},
   author = {Cini, Andrea and Zambon, Daniele and Alippi, Cesare},
   year = {2023},
   journal = {Journal of Machine Learning Research},
   volume = {24},
   number = {242},
   pages = {1--36},
   issn = {1533-7928}
}

Understanding Pooling in Graph Neural Networks, IEEE TNNLS, 2024, Grattarola, Zambon, Bianchi, Alippi.

[ DOI]
[ arXiv]
[ GitHub]
[ bibtex]

@article{grattarola2024understanding,
   title = {Understanding {{Pooling}} in {{Graph Neural Networks}}},
   author = {Grattarola, Daniele and Zambon, Daniele and Bianchi, Filippo Maria and Alippi, Cesare},
   year = {2024},
   month = feb,
   journal = {IEEE Transactions on Neural Networks and Learning Systems},
   volume = {35},
   number = {2},
   pages = {2708--2718},
   doi = {10.1109/TNNLS.2022.3190922}
}

Change-Point Methods on a Sequence of Graphs, IEEE TSP, 2019, Zambon, Alippi, Livi.

[ DOI]
[ arXiv]
[ GitHub]
[ bibtex]

@article{zambon2019changepoint,
  title = {Change-{{Point Methods}} on a {{Sequence}} of {{Graphs}}},
  author = {Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo},
  year = {2019},
  month = dec,
  journal = {IEEE Transactions on Signal Processing},
  volume = {67},
  number = {24},
  pages = {6327--6341},
  issn = {1941-0476},
  doi = {10.1109/TSP.2019.2953596}
}

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds, IEEE TNNLS, 2020, Grattarola, Zambon, Livi, Alippi.

[ DOI]
[ arXiv]
[ GitHub]
[ bibtex]

@article{grattarola2020change,
  title = {Change {{Detection}} in {{Graph Streams}} by {{Learning Graph Embeddings}} on {{Constant-Curvature Manifolds}}},
  author = {Grattarola, Daniele and Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare},
  year = {2020},
  month = jun,
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {31},
  number = {6},
  pages = {1856--1869},
  issn = {2162-237X, 2162-2388},
  doi = {10.1109/TNNLS.2019.2927301},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}
}

Concept Drift and Anomaly Detection in Graph Streams, IEEE TNNLS, 2018, Zambon, Alippi, Livi.

[ DOI]
[ arXiv]
[ experiments]
[ bibtex]

@article{zambon2018concept,
  title = {Concept {{Drift}} and {{Anomaly Detection}} in {{Graph Streams}}},
  author = {Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo},
  year = {2018},
  month = nov,
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {29},
  number = {11},
  pages = {5592--5605},
  issn = {2162-2388},
  doi = {10.1109/TNNLS.2018.2804443}
}

Conferences

Temporal Graph ODEs for Irregularly-Sampled Time Series, IJCAI, 2024, Gravina, Zambon, Bacciu, Alippi.

[ arXiv]
[ GitHub]
[ bibtex]

@inproceedings{gravina2024temporal,
   title = {Temporal Graph {{ODEs}} for Irregularly-Sampled Time Series},
   booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, {IJCAI-24}},
   publisher = {International Joint Conferences on Artificial Intelligence Organization},
   author = {Gravina, Alessio and Zambon, Daniele and Bacciu, Davide and Alippi, Cesare},
   year = {2024}
}

Graph-Based Virtual Sensing from Sparse and Partial Multivariate Observations, ICLR, 2024, De Felice, Cini, Zambon, Gusev, Alippi.

[ arXiv]
[OpenReview]
[ GitHub]
[ bibtex]
[ video]

@inproceedings{defelice2024graphbased,
   title={Graph-Based {{Virtual Sensing}} from {{Sparse}} and {{Partial Multivariate Observations}}}, 
   booktitle={The {{Twelfth International Conference}} on {{Learning Representations}}}, 
   author={De Felice, Giovanni and Cini, Andrea and Zambon, Daniele and Gusev, Vladimir and Alippi, Cesare},
   year={2024},
   url={https://openreview.net/forum?id=CAqdG2dy5s}
}

Graph Representation Learning, ESANN, 2023, Bacciu, Errica, Micheli, Navarin, Pasa, Podda, Zambon.

[ DOI]
[ bibtex]

@inproceedings{bacciu2023graph,
  title = {Graph Representation Learning},
  booktitle = {31st {{European Symposium}} on {{Artificial Neural Networks}}, {{Computational Intelligence}} and {{Machine Learning}}, {{ESANN}} 2023},
  author = {Bacciu, Davide and Errica, Federico and Micheli, Alessio and Navarin, Nicol{\`o} and Pasa, Luca and Podda, Marco and Zambon, Daniele},
  year = {2023},
  pages = {1--10},
  publisher = {i6doc. com},
  doi = {10.14428/esann/2023.ES2023-4},
  isbn = {978-2-87587-088-9}
}

Graph Kalman Filters, TGL @ NeurIPS, 2023, Zambon*, Alippi*.

[ arXiv]
[ bibtex]

@inproceedings{zambon2023tgl,
  title = {Graph {{Kalman Filters}}},
  booktitle = {Temporal {{Graph Learning Workshop}} @ {{NeurIPS}} 2023},
  author = {Zambon, Daniele and Alippi, Cesare},
  year = {2023},
  month = dec,
  url={https://openreview.net/forum?id=KGqtCfYJon}
}

Taming Local Effects in Graph-based Spatiotemporal Forecasting, NeurIPS, 2023, Cini*, Marisca*, Zambon, Alippi.

[ arXiv]
[OpenReview]
[ GitHub]
[ bibtex]
[ video]

@inproceedings{cini2023taming,
   title = {Taming {{Local Effects}} in {{Graph-based Spatiotemporal Forecasting}}},
   booktitle = {Advances in {{Neural Information Processing Systems}}},
   author = {Cini, Andrea and Marisca, Ivan and Zambon, Daniele and Alippi, Cesare},
   year = {2023},
   volume = {36},
   pages = {55375--55393},
   editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
   publisher = {Curran Associates, Inc.}
   url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/ad58c61c71efd5436134a3ecc87da6ea-Paper-Conference.pdf},
}

AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs, NeurIPS, 2022, Zambon, Alippi.

[ arXiv]
[OpenReview]
[ GitHub]
[ bibtex]
[ video]

@inproceedings{zambon2022azwhiteness,
  title = {{{AZ-whiteness}} Test: A Test for Signal Uncorrelation on Spatio-Temporal Graphs},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Zambon, Daniele and Alippi, Cesare},
  editor = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.},
  year = {2022},
  volume = {35},
  pages = {11975--11986},
  publisher = {Curran Associates, Inc.}
}

Deep Learning for Graphs, ESANN, 2022, Bacciu, Errica, Navarin, Pasa, Zambon.

[ DOI]
[ bibtex]

@inproceedings{bacciu2022deep,
  title = {Deep Learning for Graphs},
  booktitle = {30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {{ESANN}} 2022},
  author = {Bacciu, Davide and Errica, Federico and Navarin, Nicol{\`o} and Pasa, Luca and Zambon, Daniele},
  year = {2022},
  pages = {481--490},
  doi = {10.14428/esann/2022.ES2022-7},
  isbn = {978-2-87587-084-1},
  organization = {ESANN (i6doc. com)}
}

Graph iForest: Isolation of anomalous and outlier graphs, IEEE IJCNN, 2022, Zambon, Livi, Alippi.

[ DOI]
[ GitHub]
[ bibtex]

@inproceedings{zambon2022graph,
   title = {Graph {{iForest}}: {{Isolation}} of Anomalous and Outlier Graphs},
   shorttitle = {Graph {{iForest}}},
   booktitle = {2022 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
   author = {Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare},
   year = {2022},
   month = jul,
   pages = {1--8},
   issn = {2161-4407},
   doi = {10.1109/IJCNN55064.2022.9892295}
}

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning, IEEE IJCNN, 2022, Munari, Pasa, Zambon, Alippi, Navarin.

[ DOI]
[ GitHub]
[ bibtex]

@inproceedings{munari2022understanding,
   title = {Understanding {{Catastrophic Forgetting}} of {{Gated Linear Networks}} in {{Continual Learning}}},
   booktitle = {2022 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
   author = {Munari, Matteo and Pasa, Luca and Zambon, Daniele and Alippi, Cesare and Navarin, Nicol{\`o}},
   year = {2022},
   month = jul,
   pages = {1--8},
   issn = {2161-4407},
   doi = {10.1109/IJCNN55064.2022.9892142}
}

Erkennung von Anomalien und Veränderung in Graphsequenzen, D22, 2022, Zambon.

[ bibtex]

@inproceedings{zambon2022erkennung,
  title = {{Erkennung von Anomalien und Ver{\"a}nderung in Graphsequenzen}},
  booktitle = {{D22}},
  author = {Zambon, Daniele},
  year = {2022},
  pages = {311--320},
  editor={H{\"o}lldobler, Steffen}, 
  publisher = {K{\"o}llen Druck + Verlag GmbH},
  isbn = {978-3-88579-980-1}
}

Graph Edit Networks, ICLR, 2021, Paassen, Grattarola, Zambon, Alippi, Hammer.

[ GitLab]
[ bibtex]
[ video]

@inproceedings{paassen2021graph,
   title = {Graph Edit Networks},
   booktitle = {International Conference on Learning Representations ({{ICLR}})},
   author = {Paassen, Benjamin and Grattarola, Daniele and Zambon, Daniele and Alippi, Cesare and Hammer, Barbara Eva},
   year = {2021}
   url={https://openreview.net/forum?id=dlEJsyHGeaL}
}

Graph Random Neural Features for Distance-Preserving Graph Representations, ICML, 2020, Zambon, Alippi, Livi.

[ arXiv]
[ GitHub]
[ bibtex]
[ video]

@inproceedings{zambon2020graph, 
   title={Graph {{Random Neural Features}} for {{Distance-Preserving Graph Representations}}},
   author={Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo}, 
   booktitle={Proceedings of the 37th International Conference on Machine Learning (ICML)}, 
   pages={10968--10977}, 
   editor={Hal Daumé III and Aarti Singh}, 
   volume={119}, 
   series={Proceedings of Machine Learning Research}, 
   year={2020}, 
   month={13--18 Jul}, 
   publisher={PMLR}, 
   issn = {2640-3498}
   url={http://proceedings.mlr.press/v119/zambon20a.html}, 
}

Graph Embeddings from Random Neural Features, GRL @ NeurIPS, 2019, Zambon, Alippi, Livi.

[ GitHub]
[ bibtex]

@inproceedings{zambon2019graph,
  title = {Graph Embeddings from Random Neural Features},
  booktitle = {Advances in Neural Information Processing System ({{NeurIPS}}), Graph Representation Learning Workshop},
  author = {Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo},
  year = {2019}
}

Autoregressive Models for Sequences of Graphs, IEEE IJCNN, 2019, Zambon*, Grattarola*, Livi, Alippi.

[ DOI]
[ arXiv]
[ GitHub]
[ bibtex]

@inproceedings{zambon2019autoregressive,
  title = {Autoregressive {{Models}} for {{Sequences}} of {{Graphs}}},
  booktitle = {2019 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
  author = {Zambon, Daniele and Grattarola, Daniele and Livi, Lorenzo and Alippi, Cesare},
  year = {2019},
  month = jul,
  pages = {1--8},
  issn = {2161-4407},
  doi = {10.1109/IJCNN.2019.8852131}
}

Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings, IEEE IJCNN, 2018, Zambon, Livi, Alippi.

[ DOI]
[ arXiv]
[ experiments]
[ bibtex]

@inproceedings{zambon2018anomaly,
  title = {Anomaly and {{Change Detection}} in {{Graph Streams}} through {{Constant-Curvature Manifold Embeddings}}},
  booktitle = {2018 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
  author = {Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare},
  year = {2018},
  month = jul,
  pages = {1--7},
  issn = {2161-4407},
  doi = {10.1109/IJCNN.2018.8489762}
}

Detecting Changes in Sequences of Attributed Graphs, IEEE SSCI, 2017, Zambon, Livi, Alippi.

@inproceedings{zambon2017detecting,
  title = {Detecting Changes in Sequences of Attributed Graphs},
  booktitle = {2017 {{IEEE Symposium Series}} on {{Computational Intelligence}} ({{SSCI}})},
  author = {Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare},
  year = {2017},
  month = nov,
  pages = {1--7},
  doi = {10.1109/SSCI.2017.8285273}
}

Ecg monitoring in wearable devices by sparse models, ECML PKDD, 2016, Carrera, Rossi, Zambon, Fragneto, Boracchi.

[ DOI]
[ bibtex]

@inproceedings{carrera2016ecg,
  title = {{{ECG Monitoring}} in {{Wearable Devices}} by {{Sparse Models}}},
  booktitle = {Machine {{Learning}} and {{Knowledge Discovery}} in {{Databases}}},
  author = {Carrera, Diego and Rossi, Beatrice and Zambon, Daniele and Fragneto, Pasqualina and Boracchi, Giacomo},
  editor = {Berendt, Bettina and Bringmann, Bj{\"o}rn and Fromont, {\'E}lisa and Garriga, Gemma and Miettinen, Pauli and Tatti, Nikolaj and Tresp, Volker},
  year = {2016},
  pages = {145--160},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-319-46131-1_21},
  isbn = {978-3-319-46131-1}
}

Patents

Method for the Detecting Electrocardiogram Anomalies and Corresponding System, Patent, 2020, Rossi, Fragneto, Carrera, Boracchi, Zambon.

[ bibtex]

@patent{rossi2020method,
  title = {Method for the Detecting Electrocardiogram Anomalies and Corresponding System},
  author = {Rossi, Beatrice and Fragneto, Pasqualina and Carrera, Diego and Boracchi, Giacomo and Zambon, Daniele},
  year = {2020},
  month = apr,
  number = {US10610162B2},
  assignee = {STMicroelectronics SRL},
  nationality = {US}
}

Thesis

Anomaly and Change Detection in Sequences of Graphs, PhD Thesis, 2022, Zambon.

[ bibtex]

@phdthesis{zambon2022phdthesis, 
   title={Anomaly and Change Detection in Sequences of Graphs}, 
   school={Universit{\\`a} della Svizzera italiana}, 
   publisher={Universit{\\`a} della Svizzera italiana}, 
   author={Zambon, Daniele}, 
   year={2022},
}

(* equal contribution.)