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Graph Neural Networks (GNNs) have emerged as a flexible and powerful approach for learning over graphs.
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2021
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W. Azizian and M. Lelarge, “Expressive power of invariant and equivariant graph neural networks,” in Intl. Conf. on Learning Representations (ICLR)
2021
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Y. Xing, T. He, T. Xiao, Y. Wang, Y. Xiong, W. Xia, D. Wipf, Z. Zhang, and S. Soatto, “Learning hierarchical graph neural networks for image clustering,” in Intl. Conf. on Computer Vision (ICCV)
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K. Xu, M. Zhang, J. Li, S. S. Du, K.-I. Kawarabayashi, and S. Jegelka, “How neural networks extrapolate: From feedforward to graph neural networks,” in Intl. Conf. on Machine Learning (ICML)
2021
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