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Graph Neural Networks (GNNs) are a large class of relational models for graph processing.
A reduction of a graph to a canonical form and an algebra arising during this reduction,
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,
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Graph attention networks,
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Structured sequence modeling with graph convolutional recurrent networks,
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Learning dynamic context graphs for predicting social events,
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How powerful are graph neural networks?,
K. Xu, W. Hu, J. Leskovec, S. Jegelka, · 2019
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks,
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Provably powerful graph networks,
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Invariant and equivariant graph networks,
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Learning to represent the evolution of dynamic graphs with recurrent models,
A. Taheri, K. Gimpel, T. Berger-Wolf, · 2019
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Representation learning on graphs: Methods and applications,
W. L. Hamilton, R. Ying, J. Leskovec, · 2020
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Foundations and Modelling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey,
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Graph Type Expressivity and Transformations,
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Predicting molecular conformation via dynamic graph score matching,
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On the approximation capability of GNNs in node classification/regression tasks,
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Representation Learning for Dynamic Graphs: A Survey,
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Graph neural networks: A review of methods and applications,
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, M. Sun, · 2020
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Generalization and representational limits of graph neural networks,
V. Garg, S. Jegelka, T. Jaakkola, · 2020
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Inductive representation learning on temporal graphs,
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Temporal graph networks for deep learning on dynamic graphs,
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Temp: Temporal message passing for temporal knowledge graph completion,
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A survey on embedding dynamic graphs,
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Identity-aware graph neural networks,
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Nested graph neural networks,
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The surprising power of graph neural networks with random node initialization,
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Expressive power of invariant and equivariant graph neural networks,
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges,
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Theory of graph neural networks: Representation and learning,
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Graph neural networks designed for different graph types: A survey,
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Dynamic network embedding survey,
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Weisfeiler and leman go relational,
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Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities,
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