| Machine learning meets complex networks via coalescent embedding in the hyperbolic space A Muscoloni, JM Thomas, S Ciucci, G Bianconi, CV Cannistraci Nature communications 8 (1), 1615, 2017 | 196 | 2017 |
| Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks S Daminelli, JM Thomas, C Durán, CV Cannistraci New Journal of Physics 17 (11), 113037, 2015 | 150 | 2015 |
| Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory C Durán, S Daminelli, JM Thomas, VJ Haupt, M Schroeder, ... Briefings in bioinformatics 19 (6), 1183-1202, 2018 | 63 | 2018 |
| Graph Neural Networks Designed for Different Graph Types: A Survey Josephine M. Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara ... Transactions on Machine Learning Research, 2023 | 25* | 2023 |
| Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space JM Thomas, A Muscoloni, S Ciucci, G Bianconi, CV Cannistraci arXiv preprint arXiv:1602.06522, 2016 | 9 | 2016 |
| FDGNN: Fully Dynamic Graph Neural Network A Moallemy-Oureh, S Beddar-Wiesing, R Nather, JM Thomas arXiv preprint arXiv:2206.03469, 2022 | 5 | 2022 |
| Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of graph neural networks for attributed and dynamic graphs S Beddar-Wiesing, GA D’Inverno, C Graziani, V Lachi, A Moallemy-Oureh, ... Neural Networks, 106213, 2024 | 4 | 2024 |
| Power flow forecasts at transmission grid nodes using Graph Neural Networks D Beinert, C Holzhüter, JM Thomas, S Vogt Energy and AI 14, 100262, 2023 | 3 | 2023 |
| A Note on the Modeling Power of Different Graph Types JM Thomas, S Beddar-Wiesing, A Moallemy-Oureh, R Nather arXiv preprint arXiv:2109.10708, 2021 | 1 | 2021 |
| Machine learning meets complex networks via coalescent embedding of networks in the hyperbolic space A Muscoloni, JM Thomas, S Ciucci, G Bianconi, CV Cannistraci BOOK OF ABSTRACTS, 211, 2017 | 1 | 2017 |
| Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network S Beddar-Wiesing, A Moallemy-Oureh, R Nather, J Thomas Temporal Graph Learning Workshop@ NeurIPS 2023, 2023 | | 2023 |
