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Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks.
The use of multiple measurements in taxonomic problems
Ronald A Fisher · 1936
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Graph neural networks with heterophily
Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra · 2009
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Political homophily and collaboration in regional planning networks
Elisabeth R Gerber, Adam Douglas Henry, and Mark Lubell · 2013
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Homophily and missing links in citation networks
Valerio Ciotti, Moreno Bonaventura, Vincenzo Nicosia, Pietro Panzarasa, and Vito Latora · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Community detection in networks: A user guide
Santo Fortunato and Darko Hric · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an · 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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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Contextual stochastic block models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, and Elchanan Mossel · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Networks
Mark Newman · 2018
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A quest for structure: jointly learning the graph structure and semi-supervised classification
Xuan Wu, Lingxiao Zhao, and Leman Akoglu · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 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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MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Aseem Baranwal, Kimon Fountoulakis, and Aukosh Jagannath · 2021
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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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Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Enyan Dai and Suhang Wang · 2021
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Skeleton-based action recognition with directed graph neural networks
Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
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Junteng Jia and Austin R Benson · 2021
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Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim · 2021
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Is heterophily a real nightmare for graph neural networks to do node classification?
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2021
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Improving graph neural networks with simple architecture design
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata · 2021
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Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
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Graph neural networks for friend ranking in large-scale social platforms
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah · 2021
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Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma · 2021
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Synthetic graph generation to benchmark graph learning
Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, and Bryan Perozzi · 2021
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Interpreting and unifying graph neural networks with an optimization framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui · 2021
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