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Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.
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Homophily and contagion are generically confounded in observational social network studies
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Daniel A Spielman and Shang-Hua Teng · 2011
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Attributed graph models: modeling network structure with correlated attributes
Joseph J. Pfeiffer III, Sebastián Moreno, Timothy La Fond, Jennifer Neville, and Brian Gallagher · 2014
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Convolutional networks on graphs for learning molecular fingerprints
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Xiaowen Dong, Dorina Thanou, Pascal Frossard, and Pierre Vandergheynst · 2016
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William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Thomas N. Kipf and Max Welling · 2017
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Andreas Loukas and Pierre Vandergheynst · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
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Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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A unified view on graph neural networks as graph signal denoising
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
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Fast graph representation learning with pytorch geometric
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Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
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Hongyang Gao and Shuiwang Ji · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
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Deepgcns: Can gcns go as deep as cnns?
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Combining label propagation and simple models out-performs graph neural networks
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