Mingyu Yan

1.0k total citations · 1 hit paper
36 papers, 603 citations indexed

About

Mingyu Yan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Hardware and Architecture. According to data from OpenAlex, Mingyu Yan has authored 36 papers receiving a total of 603 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 10 papers in Hardware and Architecture. Recurrent topics in Mingyu Yan's work include Advanced Graph Neural Networks (18 papers), Graph Theory and Algorithms (14 papers) and Parallel Computing and Optimization Techniques (9 papers). Mingyu Yan is often cited by papers focused on Advanced Graph Neural Networks (18 papers), Graph Theory and Algorithms (14 papers) and Parallel Computing and Optimization Techniques (9 papers). Mingyu Yan collaborates with scholars based in China, United States and Australia. Mingyu Yan's co-authors include Xiaochun Ye, Dongrui Fan, Lei Deng, Yuan Xie, Zhimin Zhang, Xing Hu, Yujing Feng, Ling Liang, Guoqi Li and Xin Liu and has published in prestigious journals such as Proceedings of the IEEE, Neural Networks and IEEE Transactions on Computers.

In The Last Decade

Mingyu Yan

30 papers receiving 590 citations

Hit Papers

HyGCN: A GCN Accelerator with Hybrid Architecture 2020 2026 2022 2024 2020 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. HyGCN: A GCN Accelerator with Hybrid Architecture
    2020 216 citations Mingyu Yan, Lei Deng et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mingyu Yan China 11 391 322 182 109 105 36 603
Brian Van Essen United States 17 333 0.9× 201 0.6× 127 0.7× 299 2.7× 220 2.1× 32 773
Jungwook Choi South Korea 15 352 0.9× 352 1.1× 362 2.0× 111 1.0× 155 1.5× 54 786
Jintao Zhang China 10 250 0.6× 181 0.6× 635 3.5× 114 1.0× 120 1.1× 31 957
Tianqi Wang China 13 377 1.0× 180 0.6× 502 2.8× 252 2.3× 111 1.1× 42 854
Adnan Siraj Rakin United States 12 549 1.4× 180 0.6× 307 1.7× 92 0.8× 141 1.3× 31 774
Guohao Dai China 10 157 0.4× 160 0.5× 199 1.1× 84 0.8× 113 1.1× 46 432
Dipankar Das India 8 221 0.6× 347 1.1× 171 0.9× 176 1.6× 219 2.1× 16 617
Hyunjoon Kim Singapore 15 221 0.6× 134 0.4× 407 2.2× 90 0.8× 115 1.1× 23 595
Youwei Zhuo United States 10 283 0.7× 342 1.1× 254 1.4× 248 2.3× 286 2.7× 15 679

Countries citing papers authored by Mingyu Yan

Since Specialization
Citations

This map shows the geographic impact of Mingyu Yan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mingyu Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingyu Yan more than expected).

Fields of papers citing papers by Mingyu Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mingyu Yan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mingyu Yan. The network helps show where Mingyu Yan may publish in the future.

Co-authorship network of co-authors of Mingyu Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Mingyu Yan. A scholar is included among the top collaborators of Mingyu Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mingyu Yan. Mingyu Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wu, Meng, Mingyu Yan, Wenming Li, et al.. (2025). Survey on Characterizing and Understanding GNNs From a Computer Architecture Perspective. IEEE Transactions on Parallel and Distributed Systems. 36(3). 537–552. 3 indexed citations
2.
Yan, Mingyu, et al.. (2025). SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(9). 3490–3503.
3.
4.
Sun, Chao, Mingyu Yan, Yang Yang, et al.. (2025). In Situ Steam-Assisted Synthesis of CTAB-Modified Geopolymer-Based Hectorite for Enhanced Adsorption of Congo Red. Gels. 11(11). 930–930. 1 indexed citations
5.
Yan, Mingyu, et al.. (2024). Characterizing and Understanding HGNN Training on GPUs. ACM Transactions on Architecture and Code Optimization. 22(1). 1–25. 1 indexed citations
6.
7.
Yan, Mingyu, Xiaocheng Yang, Duo Wang, et al.. (2024). HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation. IEEE Transactions on Parallel and Distributed Systems. 35(7). 1122–1138. 4 indexed citations
8.
Yan, Mingyu, Shirui Pan, Songwen Pei, et al.. (2024). DropNaE: Alleviating irregularity for large-scale graph representation learning. Neural Networks. 183. 106930–106930.
9.
10.
Wang, Duo, et al.. (2023). MoDSE: A High-Accurate Multiobjective Design Space Exploration Framework for CPU Microarchitectures. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(5). 1525–1537. 1 indexed citations
11.
Yan, Mingyu, et al.. (2023). A Comprehensive Survey on Distributed Training of Graph Neural Networks. Proceedings of the IEEE. 111(12). 1572–1606. 22 indexed citations
12.
Yan, Mingyu, Xiaocheng Yang, Wenming Li, et al.. (2022). Characterizing and Understanding HGNNs on GPUs. IEEE Computer Architecture Letters. 21(2). 69–72. 9 indexed citations
13.
Yan, Mingyu, et al.. (2022). Characterizing and Understanding Distributed GNN Training on GPUs. IEEE Computer Architecture Letters. 21(1). 21–24. 10 indexed citations
14.
Yan, Mingyu, Duo Wang, Wenming Li, et al.. (2022). Multi-node Acceleration for Large-scale GCNs. IEEE Transactions on Computers. 1–12. 5 indexed citations
15.
Han, Pingli, Xuan Li, Fei Liu, et al.. (2022). Accurate Passive 3D Polarization Face Reconstruction under Complex Conditions Assisted with Deep Learning. Photonics. 9(12). 924–924. 12 indexed citations
16.
Liang, Ling, Lei Deng, Mingyu Yan, et al.. (2021). Fast Search of the Optimal Contraction Sequence in Tensor Networks. IEEE Journal of Selected Topics in Signal Processing. 15(3). 574–586. 5 indexed citations
17.
Yan, Mingyu, Xiaocheng Yang, Lei Deng, et al.. (2021). Hardware Acceleration for GCNs via Bidirectional Fusion. IEEE Computer Architecture Letters. 20(1). 66–69. 5 indexed citations
18.
Chen, Xiaobing, Xinfeng Xie, Xing Hu, et al.. (2021). Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(4). 936–949. 34 indexed citations
19.
Yan, Mingyu, Zhaodong Chen, Lei Deng, et al.. (2020). Characterizing and Understanding GCNs on GPU. IEEE Computer Architecture Letters. 19(1). 22–25. 43 indexed citations
20.
Liu, Fei, et al.. (2020). Polarization-based extracting diffuse reflection from light-field of object surface. Acta Physica Sinica. 69(23). 234201–234201. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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