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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu, et al.
(2020), pp. 15-29
Open Access | Times Cited: 259

Showing 1-25 of 259 citing articles:

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Tong Geng, Ang Li, Runbin Shi, et al.
(2020)
Open Access | Times Cited: 220
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 9, pp. 1-38
Open Access | Times Cited: 200
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Hanrui Wang, Kuan Wang, Jiacheng Yang, et al.
(2020)
Open Access | Times Cited: 186
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
Shengwen Liang, Ying Wang, Cheng Liu, et al.
IEEE Transactions on Computers (2020) Vol. 70, Iss. 9, pp. 1511-1525
Open Access | Times Cited: 140
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 8, pp. 8846-8885
Open Access | Times Cited: 81
Trustworthy Graph Neural Networks: Aspects, Methods, and Trends
He Zhang, Bang Ye Wu, Xingliang Yuan, et al.
Proceedings of the IEEE (2024) Vol. 112, Iss. 2, pp. 97-139
Open Access | Times Cited: 30
GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks
Jiajun Li, Ahmed Louri, Avinash Kodi, et al.
(2021)
Closed Access | Times Cited: 96
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
Xin Liu, Mingyu Yan, Lei Deng, et al.
IEEE/CAA Journal of Automatica Sinica (2021) Vol. 9, Iss. 2, pp. 205-234
Open Access | Times Cited: 83
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
Shail Dave, Riyadh Baghdadi, Tony Nowatzki, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 10, pp. 1706-1752
Open Access | Times Cited: 65
GRIP: A Graph Neural Network Accelerator Architecture
Kevin Kiningham, Philip Levis, Christopher Ré
IEEE Transactions on Computers (2022) Vol. 72, Iss. 4, pp. 914-925
Open Access | Times Cited: 47
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication
Linghao Song, Yuze Chi, Atefeh Sohrabizadeh, et al.
(2022)
Open Access | Times Cited: 45
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
Rishov Sarkar, Stefan Abi-Karam, Yuqi He, et al.
(2023), pp. 1099-1112
Open Access | Times Cited: 37
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks
Ranggi Hwang, Minhoo Kang, Jiwon Lee, et al.
(2023)
Open Access | Times Cited: 23
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
Maciej Besta, Torsten Hoefler
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 5, pp. 2584-2606
Open Access | Times Cited: 14
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Y. Iiyama, Gianluca Cerminara, Abhijay Gupta, et al.
Frontiers in Big Data (2021) Vol. 3
Open Access | Times Cited: 53
BoostGCN: A Framework for Optimizing GCN Inference on FPGA
Bingyi Zhang, Rajgopal Kannan, Viktor K. Prasanna
(2021)
Closed Access | Times Cited: 51
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
Haoran You, Tong Geng, Yongan Zhang, et al.
(2022)
Open Access | Times Cited: 37
Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures
Yu Huang, Long Zheng, Pengcheng Yao, et al.
(2022), pp. 1029-1042
Closed Access | Times Cited: 35
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Ye Wu, Xingliang Yuan, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 35
SmartSAGE
Y. Lee, Jinha Chung, Minsoo Rhu
(2022)
Closed Access | Times Cited: 28
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Haiyang Lin, Mingyu Yan, Xiaochun Ye, et al.
Proceedings of the IEEE (2023) Vol. 111, Iss. 12, pp. 1572-1606
Open Access | Times Cited: 20
HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan, et al.
IEEE Transactions on Parallel and Distributed Systems (2024) Vol. 35, Iss. 7, pp. 1122-1138
Open Access | Times Cited: 6
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation
Yu Feng, Boyuan Tian, Tiancheng Xu, et al.
(2020)
Open Access | Times Cited: 45
SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems
Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwaśniewski, et al.
(2021)
Open Access | Times Cited: 40
Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training
Xiaobing Chen, Yuke Wang, Xinfeng Xie, et al.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2021) Vol. 41, Iss. 4, pp. 936-949
Closed Access | Times Cited: 39
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