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Graph neural networks have been extensively studied for learning with inter-connected data.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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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 Gallagher, and Tina Eliassi-Rad · 2008
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Inferring networks of substitutable and complementary products
Julian J. McAuley, Rahul Pandey, and Jure Leskovec · 2015
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Geometric deep learning: going beyond euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning steady-states of iterative algorithms over graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, and Le Song · 2018
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Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 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
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
Cited alongside, same era.
Semi-supervised learning with graph learning-convolutional networks
Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo · 2019
Cited alongside, same era.
Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
Cited alongside, same era.
Graph convolutional networks for text classification
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
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Fast graph attention networks using effective resistance based graph sparsification
Rakshith Sharma Srinivasa, Cao Xiao, Lucas Glass, Justin Romberg, and Jimeng Sun · 2020
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Graph information bottleneck
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec · 2020
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
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Gnnguard: Defending graph neural networks against adversarial attacks
Xiang Zhang and Marinka Zitnik · 2020
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Robust graph representation learning via neural sparsification
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Cited alongside, same era.
GNN explainer: A tool for post-hoc explanation of graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Üstebay · 2019
Cited alongside, same era.
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed J. Zaki · 2020
Cited alongside, same era.
Latent patient network learning for automatic diagnosis
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael M. Bronstein · 2020
Cited alongside, same era.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
Cited alongside, same era.
Variational inference for graph convolutional networks in the absence of graph data and adversarial settings
Pantelis Elinas, Edwin V. Bonilla, and Louis C. Tiao · 2020
Cited alongside, same era.
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Rethinking attention with performers
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J. Colwell, and Adrian Weller · 2021
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Room-and-object aware knowledge reasoning for remote embodied referring expression
Chen Gao, Jinyu Chen, Si Liu, Luting Wang, Qiong Zhang, and Qi Wu · 2021
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim · 2021
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Learning to drop: Robust graph neural network via topological denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang · 2021
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Towards open-world feature extrapolation: An inductive graph learning approach
Qitian Wu, Chenxiao Yang, and Junchi Yan · 2021
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Towards open-world recommendation: An inductive model-based collaborative filtering approach
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, and Hongyuan Zha · 2021
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Deep graph structure learning for robust representations: A survey
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Variational inference for training graph neural networks in low-data regime through joint structure-label estimation
Danning Lao, Xinyu Yang, Qitian Wu, and Junchi Yan · 2022
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Geometric knowledge distillation: Topology compression for graph neural networks
Chenxiao Yang, Qitian Wu, and Junchi Yan · 2022
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Scalegcn: Efficient and effective graph convolution via channel-wise scale transformation
Tianqi Zhang, Qitian Wu, Junchi Yan, Yunan Zhao, and Bing Han · 2022
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