| 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 | 11 | 2022 |
| Graph type expressivity and transformations JM Thomas, S Beddar-Wiesing, A Moallemy-Oureh, R Nather arXiv preprint arXiv:2109.10708 9, 2021, 2021 | 7 | 2021 |
| 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, ... arXiv preprint arXiv:2210.03990, 2022 | 2 | 2022 |
| FDGNN: Fully Dynamic Graph Neural Network A Moallemy-Oureh, S Beddar-Wiesing, R Nather, JM Thomas arXiv preprint arXiv:2206.03469, 2022 | 1 | 2022 |
| 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 |
| 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 |
