Da Yan

2.6k total citations
109 papers, 1.5k citations indexed

About

Da Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Da Yan has authored 109 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 36 papers in Artificial Intelligence and 34 papers in Information Systems. Recurrent topics in Da Yan's work include Graph Theory and Algorithms (28 papers), Advanced Graph Neural Networks (21 papers) and Data Management and Algorithms (20 papers). Da Yan is often cited by papers focused on Graph Theory and Algorithms (28 papers), Advanced Graph Neural Networks (21 papers) and Data Management and Algorithms (20 papers). Da Yan collaborates with scholars based in United States, Hong Kong and China. Da Yan's co-authors include James Cheng, Wilfred Ng, Yi Lu, Zhou Zhao, Huanhuan Wu, Yingyi Bu, Wei‐Shinn Ku, Zhe Jiang, Yuanyuan Tian and John C. S. Lui and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Da Yan

99 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Da Yan United States 21 779 599 490 387 357 109 1.5k
Yingxia Shao China 22 574 0.7× 1.1k 1.8× 418 0.9× 287 0.7× 139 0.4× 84 1.5k
Özgür Ulusoy Türkiye 24 556 0.7× 381 0.6× 478 1.0× 998 2.6× 339 0.9× 121 1.9k
Pak Chung Wong United States 22 1.1k 1.4× 511 0.9× 224 0.5× 164 0.4× 400 1.1× 79 1.8k
Lei Zhao China 21 281 0.4× 697 1.2× 418 0.9× 282 0.7× 252 0.7× 156 1.4k
Toyotaro Suzumura Japan 21 391 0.5× 427 0.7× 382 0.8× 553 1.4× 106 0.3× 101 1.4k
Sibo Wang China 18 307 0.4× 460 0.8× 197 0.4× 394 1.0× 376 1.1× 100 1.2k
Rui Mao China 21 257 0.3× 556 0.9× 222 0.5× 550 1.4× 286 0.8× 138 1.5k
Liang Xiong China 14 299 0.4× 543 0.9× 478 1.0× 331 0.9× 121 0.3× 38 1.3k
Bo Wu United States 23 711 0.9× 634 1.1× 309 0.6× 363 0.9× 80 0.2× 90 1.6k
Lidan Shou China 18 227 0.3× 479 0.8× 275 0.6× 260 0.7× 324 0.9× 110 1.2k

Countries citing papers authored by Da Yan

Since Specialization
Citations

This map shows the geographic impact of Da 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 Da Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Da Yan more than expected).

Fields of papers citing papers by Da Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Da 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 Da Yan. The network helps show where Da Yan may publish in the future.

Co-authorship network of co-authors of Da Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Da Yan. A scholar is included among the top collaborators of Da 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 Da Yan. Da 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.
Yan, Da & Ligang He. (2025). DP-PINN+: A Dual-Phase PINN learning with automated phase division. Journal of Computational Science. 90. 102637–102637.
2.
Yan, Da, et al.. (2024). Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim. ACM Transactions on Spatial Algorithms and Systems. 11(4). 1–31.
3.
Xiao, Yang, Zijie Zhang, Yuchen Wang, et al.. (2024). Advancing Certified Robustness of Explanation via Gradient Quantization. 2596–2606.
4.
5.
Yan, Da, et al.. (2024). Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data. ACM Transactions on Spatial Algorithms and Systems. 11(2). 1–29.
6.
Smith, Adam, et al.. (2023). Machine learning the relationship between Debye temperature and superconducting transition temperature. Physical review. B.. 108(17). 4 indexed citations
7.
Yan, Da, Adam Smith, & Cheng-Chien Chen. (2023). Structure prediction and materials design with generative neural networks. Nature Computational Science. 3(7). 572–574. 28 indexed citations
8.
Wu, Hsiang‐Yun, Karsten Klein, & Da Yan. (2023). Effective Network Analytics: Network Visualization and Graph Data Management. IEEE Computer Graphics and Applications. 43(3). 10–11. 1 indexed citations
9.
Yan, Da, et al.. (2022). Mining Order-preserving Submatrices under Data Uncertainty: A Possible-world Approach and Efficient Approximation Methods. ACM Transactions on Database Systems. 47(2). 1–57. 2 indexed citations
10.
Yan, Da, et al.. (2022). Distributed Task-Based Training of Tree Models. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 2237–2249. 2 indexed citations
11.
Yan, Da, et al.. (2022). Maximal Directed Quasi -Clique Mining. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 1900–1913. 11 indexed citations
12.
Chen, Wei-Chih, et al.. (2021). Machine learning and evolutionary prediction of superhard B-C-N compounds. npj Computational Materials. 7(1). 46 indexed citations
13.
Yan, Da, et al.. (2020). G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph. 1369–1380. 30 indexed citations
14.
15.
Chen, Hongzhi, et al.. (2019). Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(2). 731–744. 2 indexed citations
16.
Yan, Da, Hongzhi Chen, James Cheng, Zhenkun Cai, & Bin Shao. (2018). Scalable De Novo Genome Assembly Using Pregel. 1216–1219. 4 indexed citations
17.
Chen, Hongzhi, et al.. (2018). G-Miner. 1–12. 70 indexed citations
18.
Yan, Da, Yingyi Bu, Yuanyuan Tian, & Amol Deshpande. (2017). Big Graph Analytics Platforms. 7(1-2). 1–195. 48 indexed citations
19.
Shang, Fanhua, Yuanyuan Liu, James Cheng, & Da Yan. (2017). Fuzzy Double Trace Norm Minimization for Recommendation Systems. IEEE Transactions on Fuzzy Systems. 26(4). 2039–2049. 25 indexed citations
20.
Wu, Huanhuan, James Cheng, Yi Lu, et al.. (2015). Core decomposition in large temporal graphs. 649–658. 51 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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