Da Yan
Impact in
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- Graph Theory and Algorithms
- Signal Processing top 2%
- Data Management and Algorithms
Papers in
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- Graph Theory and Algorithms 28
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- Advanced Graph Neural Networks 21
- Co-authors
- James Cheng (19 shared papers)Wilfred Ng (18 shared papers)Yi Lu (6 shared papers)Zhou Zhao (10 shared papers)Huanhuan Wu (4 shared papers)Yingyi Bu (4 shared papers)Zhe Jiang (22 shared papers)Wei‐Shinn Ku (11 shared papers)
- Journals
- Proceedings of the VLDB Endowment (6 papers)IEEE Transactions on Knowledge and Data Engineering (4 papers)The VLDB Journal (3 papers)ACM Transactions on Spatial Algorithms and Systems (2 papers)Knowledge and Information Systems (2 papers)
- Partner nations
- United StatesHong KongChina
In The Last Decade
Da Yan
100 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 95
- Computer Vision and Pattern Recognition 792
- Signal Processing 365
- Information Systems 494
- Artificial Intelligence 611
- Computer Networks and Communications 400
Countries citing papers authored by Da Yan
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
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-authors
The 25 scholars most cited alongside Da Yan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 110 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 160 | |
| 2 | 2014 | 87 | |
| 3 | 2018 | 71 | |
| 4 | 2015 | 69 | |
| 5 | 2014 | 68 | |
| 6 | 2015 | 52 | |
| 7 | 2017 | 49 | |
| 8 | 2021 | 48 | |
| 9 | 2016 | 47 | |
| 10 | 2011 | 39 | |
| 11 | 2013 | 38 | |
| 12 | 2016 | 32 | |
| 13 | 2020 | 30 | |
| 14 | 2023 | 30 | |
| 15 | 2011 | 27 | |
| 16 | 2017 | 27 | |
| 17 | 2017 | 26 | |
| 18 | 2021 | 24 | |
| 19 | 2012 | 23 | |
| 20 | 2013 | 22 |
About Da Yan
Da Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Signal Processing and Computer Networks and Communications, having authored 110 papers that have together received 1.5k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (28 papers), Advanced Graph Neural Networks (21 papers), Data Management and Algorithms (20 papers), Data Mining Algorithms and Applications (17 papers), Cloud Computing and Resource Management (11 papers), Transportation Planning and Optimization (10 papers), Human Mobility and Location-Based Analysis (9 papers) and Transportation and Mobility Innovations (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (792 citations), Signal Processing (365 citations), Information Systems (494 citations), Artificial Intelligence (611 citations) and Computer Networks and Communications (400 citations). Da Yan has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include James Cheng, Wilfred Ng, Yi Lu, Zhou Zhao, Huanhuan Wu, Yingyi Bu, Zhe Jiang, Wei‐Shinn Ku, Yuanyuan Tian and Cheng-Chien Chen. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Knowledge and Data Engineering, The VLDB Journal, ACM Transactions on Spatial Algorithms and Systems and Knowledge and Information Systems.
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.