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Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution.
The art and practice of structure-based drug design: a molecular modeling perspective
Regine S Bohacek, Colin McMartin, and Wayne C Guida · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Network biology: understanding the cell’s functional organization
Albert-Laszlo Barabasi and Zoltan N Oltvai · 2004
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Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Random features for large-scale kernel machines
Ali Rahimi, Benjamin Recht, et al · 2007
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Kernel measures of conditional dependence
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, and Bernhard Schölkopf · 2007
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Networks, crowds, and markets , volume 8
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How correlations influence lasso prediction
Mohamed Hebiri and Johannes Lederer · 2012
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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A new space for comparing graphs
Anshumali Shrivastava and Ping Li · 2014
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Reducing overfitting in deep networks by decorrelating representations
Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, and Dhruv Batra · 2016
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Regularizing cnns with locally constrained decorrelations
Pau Rodríguez, Jordi Gonzalez, Guillem Cucurull, Josep M Gonfaus, and Xavier Roca · 2016
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Learning graph-level representation for drug discovery
Junying Li, Deng Cai, and Xiaofei He · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Understanding disentangling in β \beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Stable prediction across unknown environments
Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Xiong, and Bo Li · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Measuring abstract reasoning in neural networks
Adam Santoro, Felix Hill, David Barrett, Ari Morcos, and Timothy Lillicrap · 2018
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Regularizing deep neural networks with an ensemble-based decorrelation method
Shuqin Gu, Yuexian Hou, Lipeng Zhang, and Yazhou Zhang · 2018
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Removing the feature correlation effect of multiplicative noise
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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Graph neural network-based diagnosis prediction
Yang Li, Buyue Qian, Xianli Zhang, and Hui Liu · 2020
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Risk prediction of theft crimes in urban communities: An integrated model of lstm and st-gcn
Xinge Han, Xiaofeng Hu, Huanggang Wu, Bing Shen, and Jiansong Wu · 2020
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An empirical study on robustness to spurious correlations using pre-trained language models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Neural execution of graph algorithms
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Zijun Zhang, Yining Zhang, and Zongpeng Li · 2018
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Learning to decompose and disentangle representations for video prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles · 2018
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Disentangled person image generation
Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, and Mario Fritz · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Using external knowledge for financial event prediction based on graph neural networks
Yiying Yang, Zhongyu Wei, Qin Chen, and Libo Wu · 2019
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Understanding attention and generalization in graph neural networks
Boris Knyazev, Graham W Taylor, and Mohamed R Amer · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2020
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Stable learning via sample reweighting
Zheyan Shen, Peng Cui, Tong Zhang, and Kun Kunag · 2020
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Stable prediction with model misspecification and agnostic distribution shift
Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, and Bo Li · 2020
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Learning de-biased representations with biased representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh · 2020
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Factorizable graph convolutional networks
Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang · 2020
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Diva: Domain invariant variational autoencoders
Maximilian Ilse, Jakub M Tomczak, Christos Louizos, and Max Welling · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron · 2021
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Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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Self-propagation graph neural network for recommendation
Wenhui Yu, Xiao Lin, Jinfei Liu, Junfeng Ge, Wenwu Ou, and Zheng Qin · 2021
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Higher-order interaction goes neural: A substructure assembling graph attention network for graph classification
Jianliang Gao, Jun Gao, Xiaoting Ying, Mingming Lu, and Jianxin Wang · 2021
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The influence of correlations between noncritical features and reinforcement on stimulus generalization
Christina J Song, Jason C Vladescu, Kenneth F Reeve, Caio F Miguel, and Samantha L Breeman · 2021
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Lasagne: A multi-layer graph convolutional network framework via node-aware deep architecture
Xupeng Miao, Wentao Zhang, Yingxia Shao, Bin Cui, Lei Chen, Ce Zhang, and Jiawei Jiang · 2021
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