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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)Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network, , , and . Workshop on Temporal Graph Learning (TGL), NeurIPS, page 1--7. (2023)Power flow forecasts at transmission grid nodes using Graph Neural Networks, , , and . Energy and AI, 14 (1): 100262 (2023)Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space., , , , and . CoRR, (2016)A Step Towards the Adoption of Standards Within the UK Ministry of Defence., , , and . Int. J. IT Stand. Stand. Res., 6 (1): 55-69 (2008)A Note on the Modeling Power of Different Graph Types, , , and . arXiv e-prints, (2021)Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory, , , , , and . Briefings in Bioinformatics, 19 (6): 1183--1202 (2017)Graph Neural Networks Designed for Different Graph Types: A Survey., , , and . Trans. Mach. Learn. Res., (2023)Graph type expressivity and transformations., , , and . CoRR, (2021)Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory., , , , , and . Briefings Bioinform., 19 (6): 1183-1202 (2018)