| Temporal graph networks for deep learning on dynamic graphs E Rossi, BP Chamberlain, F Frasca, D Eynard, F Monti, M Bronstein ICML 2020 Workshop on Graph Representation Learning, 2020 | 689 | 2020 |
| Understanding over-squashing and bottlenecks on graphs via curvature J Topping, F Di Giovanni, BP Chamberlain, X Dong, MM Bronstein ICLR 2022 (Best paper honourable mention), 2021 | 432 | 2021 |
| Sign: Scalable inception graph neural networks F Frasca, E Rossi, D Eynard, BP Chamberlain, M Bronstein, F Monti ICML 2020 Workshop on Graph Representation Learning, 2020 | 421* | 2020 |
| GRAND: Graph Neural Diffusion BP Chamberlain, J Rowbottom, M Gorinova, S Webb, E Rossi, ... The 38th International Conference on Machine Learning (ICML 2021), 2021 | 272 | 2021 |
| Neural embeddings of graphs in hyperbolic space BP Chamberlain, J Clough, MP Deisenroth SIGKDD 2017 Mining and Learning on Graphs Workshop, 2017 | 208 | 2017 |
| Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns C Bodnar, F Di Giovanni, BP Chamberlain, P Liò, MM Bronstein 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 154 | 2022 |
| Customer lifetime value prediction using embeddings BP Chamberlain, A Cardoso, CHB Liu, R Pagliari, MP Deisenroth Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 115 | 2017 |
| Graph-Coupled Oscillator Networks TK Rusch, BP Chamberlain, J Rowbottom, S Mishra, MM Bronstein The 39th International Conference on Machine Learning (ICML 2022), 2022 | 102 | 2022 |
| Graph Neural Networks for Link Prediction with Subgraph Sketching BP Chamberlain, S Shirobokov, E Rossi, F Frasca, T Markovich, ... ICLR 2023 (oral top 5%), 2022 | 92 | 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, MM Bronstein Learning on graphs conference, 11: 1-11: 16, 2022 | 85 | 2022 |
| Beltrami Flow and Neural Diffusion on Graphs BP Chamberlain, J Rowbottom, D Eynard, F Di Giovanni, X Dong, ... 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 78 | 2021 |
| Predicting twitter user socioeconomic attributes with network and language information N Aletras, BP Chamberlain Proceedings of the 29th on Hypertext and Social Media, 20-24, 2018 | 74 | 2018 |
| Scalable hyperbolic recommender systems BP Chamberlain, SR Hardwick, DR Wardrope, F Dzogang, F Daolio, ... arXiv preprint arXiv:1902.08648, 2019 | 56 | 2019 |
| Graph neural networks as gradient flows F Di Giovanni, J Rowbottom, BP Chamberlain, T Markovich, MM Bronstein | 49 | 2022 |
| Gradient Gating for Deep Multi-Rate Learning on Graphs TK Rusch, BP Chamberlain, MW Mahoney, MM Bronstein, S Mishra ICLR 2023, 2022 | 42 | 2022 |
| Generalising random forest parameter optimisation to include stability and cost CHB Liu, BP Chamberlain, DA Little, Â Cardoso Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 36 | 2017 |
| Probabilistic inference of twitter users’ age based on what they follow BP Chamberlain, C Humby, MP Deisenroth Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 35* | 2017 |
| Tuning Word2vec for Large Scale Recommendation Systems BP Chamberlain, E Rossi, D Shiebler, S Sedhain, MM Bronstein In Fourteenth ACM Conference on Recommender Systems (pp. 732-737), 2020 | 30 | 2020 |
| Fashion Outfit Generation for E-commerce EM Bettaney, SR Hardwick, O Zisimopoulos, BP Chamberlain Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 28 | 2019 |
| Understanding convolution on graphs via energies F Di Giovanni, J Rowbottom, BP Chamberlain, T Markovich, MM Bronstein arXiv preprint arXiv:2206.10991, 2022 | 27 | 2022 |
Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRA在 cs.ox.ac.uk 的电子邮件经过验证
