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While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the scalability constraint imposed by multi-hop data dependency.
Deepwalk: Online learning of social representations
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Distilling the knowledge in a neural network
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Regularizing multilayer perceptron for robustness
Prasenjit Dey, Kaustuv Nag, Tandra Pal, and Nikhil R Pal · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2019
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Seunghyun Lee and Byung Cheol Song · 2019
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Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Mary Phuong and Christoph Lampert · 2019
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Similarity-preserving knowledge distillation
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Dilin Wang, Chengyue Gong, and Qiang Liu · 2019
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How powerful are graph neural networks?
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Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 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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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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Adversarial feature augmentation and normalization for visual recognition
Tianlong Chen, Yu Cheng, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zhangyang Wang, and Jingjing Liu · 2021
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Knowledge distillation: A survey
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Graph-mlp: node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 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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Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework
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Robust pre-training by adversarial contrastive learning
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Distance encoding: Design provably more powerful neural networks for graph representation learning
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Adversarial examples improve image recognition
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Graph neural networks with heterophily
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Flag: Adversarial data augmentation for graph neural networks
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Equivariant and stable positional encoding for more powerful graph neural networks
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Knowledge distillation on graphs: A survey
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