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Self-supervision is recently surging at its new frontier of graph learning.
Asymptotic evaluation of certain markov process expectations for large time, i
Monroe D Donsker and SR Srinivasa Varadhan · 1975
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The selection of prior distributions by formal rules
Robert E Kass and Larry Wasserman · 1996
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Graph mining: Laws, generators, and algorithms
Deepayan Chakrabarti and Christos Faloutsos · 2006
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Prior selection for vector autoregressions
Domenico Giannone, Michele Lenza, and Giorgio E Primiceri · 2015
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 2017
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Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Correction by projection: Denoising images with generative adversarial networks
Subarna Tripathi, Zachary C Lipton, and Truong Q Nguyen · 2018
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Amortized variational compressive sensing
Aditya Grover and Stefano Ermon · 2018
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Task-aware compressed sensing with generative adversarial networks
Maya Kabkab, Pouya Samangouei, and Rama Chellappa · 2018
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Solving linear inverse problems using gan priors: An algorithm with provable guarantees
Viraj Shah and Chinmay Hegde · 2018
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Inference in deep networks in high dimensions
Alyson K Fletcher, Sundeep Rangan, and Philip Schniter · 2018
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Solving bilinear inverse problems using deep generative priors
Muhammad Asim, Fahad Shamshad, and Ali Ahmed · 2018
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Global guarantees for enforcing deep generative priors by empirical risk
Paul Hand and Vladislav Voroninski · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Graphgan: Graph representation learning with generative adversarial nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
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Netgan: Generating graphs via random walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Layer-dependent importance sampling for training deep and large graph convolutional networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu · 2019
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Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah · 2020
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Flag: Adversarial data augmentation for graph neural networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2019
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Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, and Jiawei Zhang · 2019
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Graphmix: Regularized training of graph neural networks for semi-supervised learning
Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang · 2019
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Towards a unified min-max framework for adversarial exploration and robustness
Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li · 2019
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Are powerful graph neural nets necessary? a dissection on graph classification
Ting Chen, Song Bian, and Yizhou Sun · 2019
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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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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, and Jie Tang · 2020
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Cross-modality protein embedding for compound-protein affinity and contact prediction
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Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec · 2020
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Graph information bottleneck for subgraph recognition
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He · 2020
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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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Graph self-supervised learning: A survey
Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S Yu · 2021
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Pre-training graph neural networks for cold-start users and items representation
Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, and Hong Chen · 2021
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How to find your friendly neighborhood: Graph attention design with self-supervision
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Self-supervised multi-channel hypergraph convolutional network for social recommendation
Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang · 2021
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Self-supervised auxiliary learning for graph neural networks via meta-learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, et al · 2021
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Michelle M Li, Kexin Huang, and Marinka Zitnik · 2021
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Hop-count based self-supervised anomaly detection on attributed networks
Tianjin Huang, Yulong Pei, Vlado Menkovski, and Mykola Pechenizkiy · 2021
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Graph-based neural network models with multiple self-supervised auxiliary tasks
Franco Manessi and Alessandro Rozza · 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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Infogcl: Information-aware graph contrastive learning
Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang · 2021
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Group contrastive self-supervised learning on graphs
Xinyi Xu, Cheng Deng, Yaochen Xie, and Shuiwang Ji · 2021
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Node embedding using mutual information and self-supervision based bi-level aggregation
Kashob Kumar Roy, Amit Roy, AKM Rahman, M Ashraful Amin, and Amin Ahsan Ali · 2021
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Automated self-supervised learning for graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang · 2021
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Priors in bayesian deep learning: A review, 2021
Vincent Fortuin · 2021
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Graphebm: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
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Graphdf: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Bootstrapped representation learning on graphs
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko · 2021
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Prototypical graph contrastive learning
Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, and Xiaodan Liang · 2021
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