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Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks.
Random graphs
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A degree sequence problem related to network design
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Spectral graph theory
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Collective dynamics of ‘small-world’ networks
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Generating random regular graphs quickly
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Statistical mechanics of complex networks
Albert, R. and Barabási, A.-L · 2002
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Accuracy and stability of numerical algorithms
Higham, N. J · 2002
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Tikhonov regularization and semi-supervised learning on large graphs
Belkin, M., Matveeva, I., and Niyogi, P · 2004
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The diameter of a scale-free random graph
Bollobás, B. and Riordan, O · 2004
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Reshuffling scale-free networks: From random to assortative
Xulvi-Brunet, R. and Sokolov, I. M · 2004
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On the stability of graph convolutional neural networks under edge rewiring
Kenlay, H., Thanou, D., and Dong, X · 2010
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Network science
Barabási, A.-L · 2013
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Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Spectral networks and deep locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
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Graph isomorphism in quasipolynomial time
Babai, L · 2016
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Taylor’s theorem for matrix functions with applications to condition number estimation
Deadman, E. and Relton, S. D · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Certifiable robustness to graph perturbations
Bojchevski, A. and Günnemann, S · 2019
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On the transferability of spectral graph filters
Levie, R., Isufi, E., and Kutyniok, G · 2019
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Levie, R., Monti, F., Bresson, X., and Bronstein, M. M · 2019
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Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Xu, K., Chen, H., Liu, S., Chen, P., Weng, T., Hong, M., and Lin, X · 2019
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Bridging the gap between spectral and spatial domains in graph neural networks
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Geometric deep learning: Going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Graph signal processing: Overview, challenges, and applications
Ortega, A., Frossard, P., Kovačević, J., Moura, J. M., and Vandergheynst, P · 2018
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Adversarial attack and defense on graph data: A survey
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Adversarial attacks on neural networks for graph data
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On the stability of polynomial spectral graph filters
Kenlay, H., Thanou, D., and Dong, X
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Balcilar, M., Renton, G., Héroux, P., Gauzere, B., Adam, S., and Honeine, P · 2020
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Machine learning on graphs: A model and comprehensive taxonomy
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Graph signal processing for machine learning: A review and new perspectives
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Stability properties of graph neural networks
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A user guide to low-pass graph signal processing and its applications: Tools and applications
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Sign: Scalable inception graph neural networks
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