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Graph convolutional networks (GCNs) are powerful tools for graph-structured data.
A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2019 · 1901
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Can Adversarial Network Attack be Defended?
Chen, J.; Wu, Y.; Lin, X.; and Xuan, Q. 2019 · 1903
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MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Abu-El-Haija, S.; Perozzi, B.; Kapoor, A.; Harutyunyan, H.; Alipourfard, N.; Lerman, K.; Steeg, G. V.; and Galstyan, A. 2019 · 1905
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Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Maehara, T. 2019 · 1905
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Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung)
Weyl, H. 1912 · 1912
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Graph convolutional encoders for syntax-aware neural machine translation
Bastings, J.; Titov, I.; Aziz, W.; Marcheggiani, D.; and Sima’an, K. 2017 · 1967
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The rotation of eigenvectors by a perturbation: III
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Stochastic blockmodels: First steps
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Spectral graph theory
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Diffusion-convolutional neural networks
Atwood, J.; and Towsley, D. 2016 · 2001
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Belkin, M.; and Niyogi, P. 2002 · 2002
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Spectral Graph Attention Network with Fast Eigen-approximation
Chang, H.; Rong, Y.; Xu, T.; Huang, W.; Sojoudi, S.; Huang, J.; and Zhu, W. 2020a · 2003
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Link-based classification
Lu, Q.; and Getoor, L. 2003 · 2003
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Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X.; Ghahramani, Z.; and Lafferty, J. D. 2003 · 2003
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A proof of Alon’s second eigenvalue conjecture and related problems
Friedman, J. 2004 · 2004
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Spectral techniques applied to sparse random graphs
Feige, U.; and Ofek, E. 2005 · 2005
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
Belkin, M.; Niyogi, P.; and Sindhwani, V. 2006 · 2006
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Noise robust spectral clustering
Li, Z.; Liu, J.; Chen, S.; and Tang, X. 2007 · 2007
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Visualizing data using t-SNE
Maaten, L. v. d.; and Hinton, G. 2008 · 2008
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Implicit graph neural networks
Gu, F.; Chang, H.; Zhu, W.; Sojoudi, S.; and Ghaoui, L. E. 2020 · 2009
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Graph partitioning via adaptive spectral techniques
Coja-Oghlan, A. 2010 · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X.; and Bengio, Y. 2010 · 2010
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Noise thresholds for spectral clustering
Balakrishnan, S.; Xu, M.; Krishnamurthy, A.; and Singh, A. 2011 · 2011
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Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
Decelle, A.; Krzakala, F.; Moore, C.; and Zdeborová, L. 2011 · 2011
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Wavelets on graphs via spectral graph theory
Hammond, D. K.; Vandergheynst, P.; and Gribonval, R. 2011 · 2011
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Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model
Chaudhuri, K.; Chung, F.; and Tsiatas, A. 2012 · 2012
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Learning to Discover Social Circles in Ego Networks
Leskovec, J.; and Mcauley, J. J. 2012 · 2012
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Stochastic block models and reconstruction
Mossel, E.; Neeman, J.; and Sly, A. 2012 · 2012
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Deep Learning via Semi-Supervised Embedding
Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
Marcheggiani, D.; and Titov, I. 2017 · 2017
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Graph powering and spectral robustness
Abbe, E.; Boix, E.; Ralli, P.; and Sandon, C. 2018 · 2018
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N-GCN: Multi-scale graph convolution for semi-supervised node classification
Abu-El-Haija, S.; Kapoor, A.; Perozzi, B.; and Lee, J. 2018 · 2018
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Weston, J.; Ratle, F.; Mobahi, H.; and Collobert, R. 2012 · 2012
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A Proof Of The Block Model Threshold Conjecture
Mossel, E.; Neeman, J.; and Sly, A. 2013 · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J.; Zaremba, W.; Szlam, A.; and LeCun, Y. 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Multiway spectral partitioning and higher-order cheeger inequalities
Lee, J. R.; Gharan, S. O.; and Trevisan, L. 2014 · 2014
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Community detection thresholds and the weak Ramanujan property
Massoulié, L. 2014 · 2014
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Deepwalk: Online learning of social representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
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Adversarial Attack on Graph Structured Data
Dai, H.; Li, H.; Tian, T.; Huang, X.; Wang, L.; Zhu, J.; and Song, L. 2018 · 2018
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i-RevNet: Deep Invertible Networks
Jacobsen, J.-H.; Smeulders, A.; and Oyallon, E. 2018 · 2018
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Higher-order graph convolutional networks
Lee, J. B.; Rossi, R. A.; Kong, X.; Kim, S.; Koh, E.; and Rao, A. 2018 · 2018
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Adversarial Attack and Defense on Graph Data: A Survey
Sun, L.; Wang, J.; Yu, P. S.; and Li, B. 2018 · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R.; He, R.; Chen, K.; Eksombatchai, P.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
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Graph neural networks: A review of methods and applications
Zhou, J.; Cui, G.; Zhang, Z.; Yang, C.; Liu, Z.; and Sun, M. 2018 · 2018
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Adversarial Attacks on Node Embeddings via Graph Poisoning
Bojchevski, A.; and Günnemann, S. 2019 · 2019
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Robust Estimators in High-Dimensions Without the Computational Intractability
Diakonikolas, I.; Kamath, G.; Kane, D.; Li, J.; Moitra, A.; and Stewart, A. 2019 · 2019
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Label efficient semi-supervised learning via graph filtering
Li, Q.; Wu, X.-M.; Liu, H.; Zhang, X.; and Guan, Z. 2019 · 2019
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LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Liao, R.; Zhao, Z.; Urtasun, R.; and Zemel, R. S. 2019 · 2019
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Semi-supervised User Geolocation via Graph Convolutional Networks
Rahimi, A.; Cohn, T.; and Baldwin, T. 2018 · 2019
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Simplifying Graph Convolutional Networks
Wu, F.; Souza, A. H.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. Q. 2019 · 2019
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Robust Graph Convolutional Networks Against Adversarial Attacks
Zhu, D.; Zhang, Z.; Cui, P.; and Zhu, W. 2019 · 2019
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Adversarial Attacks on Graph Neural Networks via Meta Learning
Zügner, D.; and Günnemann, S. 2019 · 2019
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Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
Chang, H.; Rong, Y.; Xu, T.; Huang, W.; Zhang, H.; Cui, P.; Wang, X.; Zhu, W.; and Huang, J. 2021 · 2021
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AutoGL: A Library for Automated Graph Learning
Guan, C.; Zhang, Z.; Li, H.; Chang, H.; Zhang, Z.; Qin, Y.; Jiang, J.; Wang, X.; and Zhu, W. 2021 · 2021
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Pseudo-likelihood methods for community detection in large sparse networks
Amini, A. A.; Chen, A.; Bickel, P. J.; Levina, E.; et al. 2013 · 2097
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