| Understanding pooling in graph neural networks D Grattarola, D Zambon, FM Bianchi, C Alippi IEEE transactions on neural networks and learning systems 35 (2), 2708 - 2718, 2024 | 88 | 2024 |
| A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection M Jin, HY Koh, Q Wen, D Zambon, C Alippi, GI Webb, I King, S Pan arXiv preprint arXiv:2307.03759, 2023 | 76 | 2023 |
| Concept drift and anomaly detection in graph streams D Zambon, C Alippi, L Livi IEEE transactions on neural networks and learning systems 29 (11), 5592-5605, 2018 | 60 | 2018 |
| Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds D Grattarola, D Zambon, C Alippi, L Livi IEEE Transactions on Neural Networks and Learning Systems 31 (6), 1856-1869, 2020 | 55* | 2020 |
| Ecg monitoring in wearable devices by sparse models D Carrera, B Rossi, D Zambon, P Fragneto, G Boracchi Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 18 | 2016 |
| Graph edit networks B Paassen, D Grattarola, D Zambon, C Alippi, BE Hammer International Conference on Learning Representations, 2021 | 17 | 2021 |
| Graph Random Neural Features for Distance-Preserving Graph Representations D Zambon, C Alippi, L Livi International Conference on Machine Learning 119, 10968--10977, 2020 | 16 | 2020 |
| Taming local effects in graph-based spatiotemporal forecasting A Cini, I Marisca, D Zambon, C Alippi Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Change-point methods on a sequence of graphs D Zambon, C Alippi, L Livi IEEE Transactions on Signal Processing 67 (24), 6327-6341, 2019 | 11 | 2019 |
| Detecting changes in sequences of attributed graphs D Zambon, L Livi, C Alippi 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017 | 11 | 2017 |
| Graph deep learning for time series forecasting A Cini, I Marisca, D Zambon, C Alippi arXiv preprint arXiv:2310.15978, 2023 | 8 | 2023 |
| Sparse graph learning from spatiotemporal time series A Cini, D Zambon, C Alippi Journal of Machine Learning Research 24 (242), 1-36, 2023 | 8 | 2023 |
| Autoregressive models for sequences of graphs D Zambon, D Grattarola, L Livi, C Alippi 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 8 | 2019 |
| Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings D Zambon, L Livi, C Alippi 2018 International Joint Conference on Neural Networks (IJCNN), 2018 | 8 | 2018 |
| Method for the detecting electrocardiogram anomalies and corresponding system B Rossi, P Fragneto, D Carrera, G Boracchi, D Zambon US Patent 10,610,162, 2020 | 7 | 2020 |
| AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs D Zambon, C Alippi Advances in Neural Information Processing Systems 35, 11975-11986, 2022 | 6* | 2022 |
| Graph iForest: Isolation of anomalous and outlier graphs D Zambon, L Livi, C Alippi 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 4 | 2022 |
| Understanding catastrophic forgetting of gated linear networks in continual learning M Munari, L Pasa, D Zambon, C Alippi, N Navarin 2022 International joint conference on neural networks (IJCNN), 1-8, 2022 | 3 | 2022 |
| Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations G De Felice, A Cini, D Zambon, VV Gusev, C Alippi arXiv preprint arXiv:2402.12598, 2024 | 2 | 2024 |
| Graph Kalman filters C Alippi, D Zambon arXiv preprint arXiv:2303.12021, 2023 | 2 | 2023 |
Cesare AlippiUniversità della Svizzera italiana, Politecnico di Milano在 usi.ch 的电子邮件经过验证
