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Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs

, , , , , , and . Neural Networks, (2024)
DOI: 10.1016/j.neunet.2024.106213

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Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs., , , , , , and . CoRR, (2022)Graph type expressivity and transformations., , , and . CoRR, (2021)Continuous-time generative graph neural network for attributed dynamic graphs: student research abstract.. SAC, page 600-603. ACM, (2022)Graph Neural Networks Designed for Different Graph Types: A Survey., , , and . CoRR, (2022)FDGNN: Fully Dynamic Graph Neural Network., , , and . CoRR, (2022)Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs. ACM/SIGAPP Symposium on Applied Computing (SAC), page 600--603. ACM, (2022)On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs, , , , , and . Workshp on Mining and Learning on Graphs (MLG), ECML PKDD, page 1--13. (2022)Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs, , , , , , and . Neural Networks, (2024)Graph Pooling Provably Improves Expressivity, , , and . Workshop on New Frontiers in Graph Learning, NeurIPS, page 1--7. (2023)Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network, , , and . Workshop on Temporal Graph Learning (TGL), NeurIPS, page 1--7. (2023)