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Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning.
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Optimal transport: old and new , volume 338
Cédric Villani · 2008
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Social influence analysis in large-scale networks
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent · 2010
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Autoencoders, unsupervised learning, and deep architectures
Pierre Baldi · 2012
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Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
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Rolx: structural role extraction & mining in large graphs
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li · 2012
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and UMD EDU · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Auto-encoding variational bayess
Diederik P Kingma and Max Welling · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Learning with a wasserstein loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, and Tomaso A Poggio · 2015
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2016
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Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Non-local graph neural networks
Meng Liu, Zhengyang Wang, and Shuiwang Ji · 2020
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A universal approximation theorem of deep neural networks for expressing probability distributions
Yulong Lu and Jianfeng Lu · 2020
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Sinkhorn autoencoders
Giorgio Patrini, Rianne van den Berg, Patrick Forre, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, and Frank Nielsen · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Effective decoding in graph auto-encoder using triadic closure
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Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 2018
Cited alongside, same era.
Sliced wasserstein auto-encoders
Soheil Kolouri, Phillip E Pope, Charles E Martin, and Gustavo K Rohde · 2018
Cited alongside, same era.
Adversarially regularized graph autoencoder for graph embedding
S Pan, R Hu, G Long, J Jiang, L Yao, and C Zhang · 2018
Cited alongside, same era.
Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang · 2018
Cited alongside, same era.
Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
Cited alongside, same era.
Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
Cited alongside, same era.
Han Shi, Haozheng Fan, and James T Kwok · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2020
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2020
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Adaptive universal generalized pagerank graph neural network
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2021
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On positional and structural node features for graph neural networks on non-attributed graphs
Hejie Cui, Zijie Lu, Pan Li, and Carl Yang · 2021
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Graph attention networks with positional embeddings
Liheng Ma, Reihaneh Rabbany, and Adriana Romero-Soriano · 2021
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Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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Self-supervised learning of graph neural networks: A unified review
Yaochen Xie, Zhao Xu, Zhengyang Wang, and Shuiwang Ji · 2021
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Secure deep graph generation with link differential privacy
Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, and Lichao Sun · 2021
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Transfer learning of graph neural networks with ego-graph information maximization
Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han · 2021
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