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Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high energy physics.
Collective classification in network data
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Semi-supervised classification with graph convolutional networks
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Petar Veličković et al · 2017
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Neural message passing for quantum chemistry
Justin Gilmer et al · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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DrugBank 5.0: a major update to the DrugBank database for 2018
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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
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Keyulu Xu et al · 2019
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Strategies for pre-training graph neural networks, 2019
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Liyu Gong and Qiang Cheng · 2019
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Felix Wu et al · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Graph warp module: an auxiliary module for boosting the power of graph neural networks
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Pan: Path integral based convolution for deep graph neural networks
Zheng Ma, Ming Li, and Yuguang Wang · 2019
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Top quark tagging reference dataset, March 2019
Gregor Kasieczka, Tilman Plehn, Jennifer Thompson, and Michael Russel · 2019
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Point-GNN: Graph neural network for 3D object detection in a point cloud
Weijing Shi and Raj Rajkumar · 2020
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Graph neural networks in particle physics
Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant · 2020
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Distance-weighted graph neural networks on FPGAs for real-time particle reconstruction at the Large Hadron Collider
Gianluca Cerminara, Abhijay Gupta, Yutaro Iiyama, Jan Kieseler, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, et al · 2020
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ABCNet: An attention-based method for particle tagging
V. Mikuni and F. Canelli · 2020
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Huilin Qu and Loukas Gouskos · 2020
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Hardware acceleration of graph neural networks
Adam Auten, Matthew Tomei, and Rakesh Kumar · 2020
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AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing
Tong Geng, Ang Li, Runbin Shi, Chunshu Wu, Tianqi Wang, Yanfei Li, Pouya Haghi, Antonino Tumeo, Shuai Che, Steve Reinhardt, et al · 2020
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Redundancy-free computation for graph neural networks
Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken · 2020
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I-GCN: A graph convolutional network accelerator with runtime locality enhancement through islandization
Tong Geng, Chunshu Wu, Yongan Zhang, Cheng Tan, Chenhao Xie, Haoran You, Martin Herbordt, Yingyan Lin, and Ang Li · 2021
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Rubik: A hierarchical architecture for efficient graph neural network training
Xiaobing Chen, Yuke Wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, et al · 2021
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Directional graph networks
Dominique Beani, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, and Pietro Liò · 2021
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EnGN: A high-throughput and energy-efficient accelerator for large graph neural networks
Shengwen Liang, Ying Wang, Cheng Liu, Lei He, Huawei Li, Dawen Xu, and Xiaowei Li · 2021
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GCNAX: A flexible and energy-efficient accelerator for graph convolutional neural networks
Jiajun Li, Ahmed Louri, Avinash Karanth, and Razvan Bunescu · 2021
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GNNAdvisor: An efficient runtime system for GNN acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding · 2020
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HyGCN: A GCN accelerator with hybrid architecture
Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, and Yuan Xie · 2020
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AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing
Tong Geng, Ang Li, Runbin Shi, Chunshu Wu, Tianqi Wang, Yanfei Li, Pouya Haghi, Antonino Tumeo, Shuai Che, Steve Reinhardt, and Martin C. Herbordt · 2020
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HyGCN: A GCN accelerator with hybrid architecture
Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, and Yuan Xie · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso et al · 2020
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Hanqing Zeng and Viktor Prasanna · 2020
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Thundergp: Hls-based graph processing framework on fpgas
Xinyu Chen, Hongshi Tan, Yao Chen, Bingsheng He, Weng-Fai Wong, and Deming Chen · 2021
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Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification
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Do we need anisotropic graph neural networks?
Shyam A Tailor, Felix Opolka, Pietro Lio, and Nicholas Donald Lane · 2021
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Versagnn: a versatile accelerator for graph neural networks
Feng Shi, Ahren Yiqiao Jin, and Song-Chun Zhu · 2021
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Node augmentation methods for graph neural network based object classification
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Graph neural networks for charged particle tracking on FPGAs
Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang, Javier Duarte, Markus Atkinson, Gage DeZoort, Peter Elmer, Scott Hauck, Jin-Xuan Hu, Shih-Chieh Hsu, Bo-Cheng Lai, Mark Neubauer, Isobel Ojalvo, Savannah Thais, and Matthew Trahms · 2022
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Computing graph neural networks: A survey from algorithms to accelerators
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Rubik: A hierarchical architecture for efficient graph neural network training
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Message passing all the way up, 2022
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https://github.com/snap-stanford/ogb/tree/master/examples/graphproppred/mol
GNN models from Open Graph Benchmark · 2022
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StreamGCN: Accelerating graph convolutional networks with streaming processing
Atefeh Sohrabizadeh, Yuze Chi, and Jason Cong · 2022
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https://pytorch-geometric.readthedocs.io/en/latest/
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