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Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications.
Sparsification—a technique for speeding up dynamic graph algorithms
David Eppstein, Zvi Galil, Giuseppe F Italiano, and Amnon Nissenzweig · 1997
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A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
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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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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, and Eric Chu · 2011
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Recent advances in graph partitioning
Aydın Buluç, Henning Meyerhenke, Ilya Safro, and et al · 2016
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A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 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
Thomas N Kipf and Max Welling · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, and et al · 2018
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Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2018
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Fastgcn: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and et al · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, and et al · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, and et al · 2019
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Neural graph collaborative filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Alleviating irregularity in graph analytics acceleration: A hardware/software co-design approach
Mingyu Yan, Xing Hu, Shuangchen Li, Abanti Basak, Han Li, Xin Ma, Itir Akgun, Yujing Feng, Peng Gu, Lei Deng, et al · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Accurate, efficient and scalable graph embedding
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
Cited alongside, same era.
Heterogeneous graph neural network
Chuxu Zhang, Dongjin Song, Chao Huang, and et al · 2019
Cited alongside, same era.
Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, and et al · 2020
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Robust graph representation learning via neural sparsification
Cheng Zheng, Bo Zong, Wei Cheng, and et al · 2020
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Distdgl: distributed graph neural network training for billion-scale graphs
Da Zheng, Chao Ma, Minjie Wang, and et al · 2020
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Computing graph neural networks: A survey from algorithms to accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, and et al · 2021
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Dare: Droplayer-aware manycore reram architecture for training graph neural networks
Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa, and et al · 2021
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Binary graph neural networks
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Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
Cited alongside, same era.
Graph neural networks meet neural-symbolic computing: A survey and perspective
Luís C. Lamb, Artur S. d’Avila Garcez, Marco Gori, Marcelo O. R. Prates, Pedro H. C. Avelar, and Moshe Y. Vardi · 2020
Cited alongside, same era.
Sgcn: A graph sparsifier based on graph convolutional networks
Jiayu Li, Tianyun Zhang, Hao Tian, Shengmin Jin, Makan Fardad, and Reza Zafarani · 2020
Cited alongside, same era.
Pagraph: Scaling gnn training on large graphs via computation-aware caching
Zhiqi Lin, Cheng Li, Youshan Miao, and et al · 2020
Cited alongside, same era.
Mehdi Bahri, Gaétan Bahl, and Stefanos Zafeiriou · 2021
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A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, and et al · 2021
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Survey on graph neural network acceleration architectures
Li Han, Yan Mingyu, Lü Zhengyang, Li Wenming, Ye Xiaochun, Fan Dongrui, and Tang Zhimin · 2021
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Adaptivegcn: Efficient gcn through adaptively sparsifying graphs
Dongyue Li, Tao Yang, Lun Du, and et al · 2021
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Bgl: Gpu-efficient gnn training by optimizing graph data i/o and preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li, and et al · 2021
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Sampling methods for efficient training of graph convolutional networks: A survey
Xin Liu, Mingyu Yan, Lei Deng, and et al · 2021
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Gnnsampler: Bridging the gap between sampling algorithms of gnn and hardware
Xin Liu, Mingyu Yan, Shuhan Song, and et al · 2021
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Ultragcn: Ultra simplification of graph convolutional networks for recommendation
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He · 2021
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Distgnn: Scalable distributed training for large-scale graph neural networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma, and et al · 2021
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Degree-quant: Quantization-aware training for graph neural networks
Shyam Anil Tailor, Javier Fernández-Marqués, and Nicholas Donald Lane · 2021
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Bi-gcn: Binary graph convolutional network
Junfu Wang, Yunhong Wang, Zhen Yang, and et al · 2021
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Graph learning based recommender systems: A review
Shoujin Wang, Liang Hu, Yan Wang, and et al · 2021
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Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework
Cheng Yang, Jiawei Liu, and Chuan Shi · 2021
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Automated machine learning on graphs: A survey
Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2021
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Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, and et al · 2021
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