| Graph neural networks designed for different graph types: A survey JM Thomas, A Moallemy-Oureh, S Beddar-Wiesing, C Holzhüter arXiv preprint arXiv:2204.03080, 2022 | 16 | 2022 |
| Graph neural networks designed for different graph types: A survey J Thomas, A Moallemy-Oureh, S Beddar-Wiesing, CJ Holzhüter | 9 | 2023 |
| Graph type expressivity and transformations JM Thomas, S Beddar-Wiesing, A Moallemy-Oureh, R Nather arXiv preprint arXiv:2109.10708 9, 2021, 2021 | 8 | 2021 |
| 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 |
| Continuous-time generative graph neural network for attributed dynamic graphs: student research abstract A Moallemy-Oureh Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, 600-603, 2022 | 1 | 2022 |
| 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 |
| Graph Pooling Provably Improves Expressivity V Lachi, A Moallemy-Oureh, A Roth, P Welke NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023 | | 2023 |
| Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network A Moallemy-Oureh, S Beddar-Wiesing, R Nather, J Thomas Temporal Graph Learning Workshop@ NeurIPS 2023, 2023 | | 2023 |
| On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs S Beddar-Wiesing, GA D'Inverno, C Graziani, V Lachi, A Moallemy-Oureh 18th International Workshop on Mining and Learning with Graphs, 2022 | | 2022 |
