Selected news!

• [8-2024] Our survey of GNN for time series has been accepted at IEEE TPAMI.
• [4-2024] New paper on irregularly-sampled time series accepted at IJCAI ‘24!
• [1-2024] New paper on virtual sensing accepted at ICLR ‘24!

List of publications

Preprints

• Learning Latent Graph Structures and their Uncertainty, 2024, Manenti, Zambon, Alippi.
  
  • [ arXiv]
  • [ bibtex]

  @misc{manenti2024learning,
    title = {Learning {{Latent Graph Structures}} and Their {{Uncertainty}}},
    author = {Manenti, Alessandro and Zambon, Daniele and Alippi, Cesare},
    year = {2024},
    month = may,
    number = {arXiv:2405.19933},
    primaryclass = {cs, stat},
    publisher = {arXiv},
    archiveprefix = {arxiv}
  }
  

• 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}
  }
  

• 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

• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, IEEE TPAMI, 2024, Jin, Koh, Wen, Zambon, Alippi, Webb, King, Pan.
  
  • [ DOI]
  • [ arXiv]
  • [ GitHub]
  • [ bibtex]
  • [ video]

  @article{jin2024survey,
    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},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection}, 
    year={2024},
    volume={46},
    number={12},
    pages={10466-10485},
    doi={10.1109/TPAMI.2024.3443141}
  }
  

• Sparse Graph Learning from 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

• Learning Latent Graph Structures and their Uncertainty, SPIGM @ ICML, 2024, Manenti, Zambon, Alippi.
  
  • [ arXiv]
  • [OpenReview]
  • [ bibtex]

  @inproceedings{manenti2024spigm,
    title = {Learning {{Latent Graph Structures}} and Their {{Uncertainty}}},
    author = {Manenti, Alessandro and Zambon, Daniele and Alippi, Cesare},
    booktitle={ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative Modeling},
    year = {2024},
    url={https://openreview.net/forum?id=GRlNTDymoV}
  }
  

• Temporal Graph ODEs for Irregularly-Sampled Time Series, IJCAI, 2024, Gravina, Zambon, Bacciu, Alippi.
  
  • [ DOI]
  • [ 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},
    doi = {10.24963/ijcai.2024/445},
    url = {https://doi.org/10.24963/ijcai.2024/445},
  }
  

• 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 Excellent Computer Science Dissertations, 2022, Zambon.
  
  • [ bibtex]

  @inproceedings{zambon2022erkennung,
    title = {{Erkennung von Anomalien und Ver{\"a}nderung in Graphsequenzen}},
    booktitle = {{D22} Ausgezeichnete Informatikdissertationen},
    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.
  
  • [ DOI]
  • [ experiments]
  • [ bibtex]

  @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.)