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Graph neural networks (GNNs) are one of the most popular approaches to using deep learning on graph-structured data, and they have shown state-of-the-art performances on a variety of tasks.
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Thomas N Kipf and Max Welling · 2017
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
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Chip placement with deep reinforcement learning
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Andrew Trask, Felix Hill, Scott E Reed, Jack Rae, Chris Dyer, and Phil Blunsom · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Asap: Adaptive structure aware pooling for learning hierarchical graph representations
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A deep learning approach to antibiotic discovery
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Inductive representation learning on temporal graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2020
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Structpool: Structured graph pooling via conditional random fields
Hao Yuan and Shuiwang Ji · 2020
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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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Ublf: An upper bound based approach to discover influential nodes in social networks
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