| Temporal Graph Networks for Deep Learning on Dynamic Graphs E Rossi, B Chamberlain, F Frasca, D Eynard, F Monti, M Bronstein ICML 2020 Workshop on Graph Representation Learning, 2020 | 860 | 2020 |
| SIGN: Scalable Inception Graph Neural Networks E Rossi, F Frasca, B Chamberlain, D Eynard, M Bronstein, F Monti ICML 2020 Workshop on Graph Representation Learning, 2020 | 457* | 2020 |
| Grand: Graph neural diffusion B Chamberlain, J Rowbottom, MI Gorinova, M Bronstein, S Webb, E Rossi International Conference on Machine Learning (ICML 2021), 1407-1418, 2021 | 321 | 2021 |
| Graph Neural Networks for Link Prediction with Subgraph Sketching BP Chamberlain, S Shirobokov, E Rossi, F Frasca, T Markovich, ... International Conference on Learning Representations (ICLR) 2022, 2022 | 123 | 2022 |
| On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features E Rossi, H Kenlay, MI Gorinova, BP Chamberlain, X Dong, M Bronstein Learning on Graphs Conference (LoG) 2022, 2021 | 112 | 2021 |
| Temporal Graph Benchmark for Machine Learning on Temporal Graphs S Huang, F Poursafaei, J Danovitch, M Fey, W Hu, E Rossi, J Leskovec, ... NeurIPS 2023 Datasets and Benchmarks Track, 2023 | 102 | 2023 |
| Edge Directionality Improves Learning on Heterophilic Graphs E Rossi, B Charpentier, F Di Giovanni, F Frasca, S Günnemann, ... Learning on Graphs Conference (LoG) 2023, 2023 | 78 | 2023 |
| Tuning word2vec for large scale recommendation systems BP Chamberlain, E Rossi, D Shiebler, S Sedhain, MM Bronstein Proceedings of the 14th ACM Conference on Recommender Systems, 732-737, 2020 | 33 | 2020 |
| ncRNA Classification with Graph Convolutional Networks E Rossi, F Monti, M Bronstein, P Liò KDD 2019 Workshop on Deep Learning on Graphs, 2019 | 26 | 2019 |
| Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization M Mutti, R De Santi, E Rossi, JF Calderon, M Bronstein, M Restelli Proceedings of the AAAI Conference on Artificial Intelligence 2022, 2022 | 20* | 2022 |
| PLINDER: The protein-ligand interactions dataset and evaluation resource J Durairaj, Y Adeshina, Z Cao, X Zhang, V Oleinikovas, T Duignan, ... bioRxiv, 2024.07. 17.603955, 2024 | 12 | 2024 |
| Learning to Infer Structures of Network Games E Rossi, F Monti, Y Leng, MM Bronstein, X Dong International Conference on Machine Learning (ICML 2022), 2022 | 9 | 2022 |
| Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? D Malitesta, E Rossi, C Pomo, T Di Noia, FD Malliaros Proceedings of the 33rd ACM International Conference on Information and …, 2024 | 8* | 2024 |
| Tgb 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs J Gastinger, S Huang, M Galkin, E Loghmani, A Parviz, F Poursafaei, ... Advances in neural information processing systems 37, 140199-140229, 2025 | 5 | 2025 |
| PINDER: The protein interaction dataset and evaluation resource D Kovtun, M Akdel, A Goncearenco, G Zhou, G Holt, D Baugher, D Lin, ... bioRxiv, 2024.07. 17.603980, 2024 | 5 | 2024 |
| Channel Balance Interpolation in the Lightning Network via Machine Learning Vincent, E Rossi, V Singh arXiv preprint arXiv:2405.12087, 2024 | 1* | 2024 |
| Bayesian Binary Search V Singh, M Khanzadeh, V Davis, H Rush, E Rossi, J Shrader, P Lio arXiv preprint arXiv:2410.01771, 2024 | | 2024 |
| UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs S Huang, F Poursafaei, R Rabbany, G Rabusseau, E Rossi arXiv preprint arXiv:2407.12269, 2024 | | 2024 |
Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAПотвърден имейл адрес: cs.ox.ac.uk