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Alice Moallemy-Oureh
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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
182022
Graph neural networks designed for different graph types: A survey
J Thomas, A Moallemy-Oureh, S Beddar-Wiesing, CJ Holzhüter
102023
Graph type expressivity and transformations
JM Thomas, S Beddar-Wiesing, A Moallemy-Oureh, R Nather
arXiv preprint arXiv:2109.10708 9, 2021, 2021
82021
Fdgnn: Fully dynamic graph neural network
A Moallemy-Oureh, S Beddar-Wiesing, R Nather, JM Thomas
arXiv preprint arXiv:2206.03469, 2022
52022
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
42024
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
12022
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
12021
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
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