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Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs, , , , , , and . arXiv e-prints, (2022)Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction (Extended Abstract)., and . TIME, volume 278 of LIPIcs, page 19:1-19:3. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2023)A Note on the Modeling Power of Different Graph Types, , , and . arXiv e-prints, (2021)Time-aware Robustness of Temporal Graph Neural Networks for Link Prediction, and . International Symposium on Temporal Representation and Reasoning (TIME), page 19:1--19:3. (2023)Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs, , , , , , and . Neural Networks, (2024)Graph Neural Networks Designed for Different Graph Types: A Survey., , , and . Trans. Mach. Learn. Res., (2023)Using local activity encoding for dynamic graph pooling in stuctural-dynamic graphs: student research abstract.. SAC, page 604-609. ACM, (2022)Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network, , , and . Workshop on Temporal Graph Learning (TGL), NeurIPS, page 1--7. (2023)Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs. ACM/SIGAPP Symposium on Applied Computing (SAC), page 604--609. ACM, (2022)Graph Neural Networks Designed for Different Graph Types: A Survey, , , and . arXiv e-prints, (2022)