Dan Jiang
Impact in
-
- Industrial Vision Systems and Defect Detection
- Manufacturing Process and Optimization
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Topic Modeling
- Speech Recognition and Synthesis
Papers in
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- Advanced Graph Neural Networks 4
- Topic Modeling 3
- Machine Learning and Data Classification 2
-
- Complex Network Analysis Techniques 4
- Co-authors
- Nagarajan Raghavan (3 shared papers)Rongbin Xu (4 shared papers)Lixia Xue (3 shared papers)Ronggui Wang (3 shared papers)Xinmei Wang (3 shared papers)Ying Xie (3 shared papers)Ying Xie (1 shared paper)Juan Yang (2 shared papers)
- Journals
- IEEE Access (3 papers)Information Sciences (3 papers)Knowledge-Based Systems (2 papers)Expert Systems with Applications (1 paper)Physics of Plasmas (1 paper)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Dan Jiang
18 papers receiving 286 citations
Peers
Comparison fields: 5 of 63
- Industrial and Manufacturing Engineering 53
- Artificial Intelligence 129
- Statistical and Nonlinear Physics 41
- Computational Mathematics 2
- Hardware and Architecture 13
Countries citing papers authored by Dan Jiang
This map shows the geographic impact of Dan Jiang'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 Dan Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Jiang more than expected).
Fields of papers citing papers by Dan Jiang
This network shows the impact of papers produced by Dan Jiang. 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 Dan Jiang. The network helps show where Dan Jiang may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Jiang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 49 | |
| 2 | 2019 | 39 | |
| 3 | 2021 | 31 | |
| 4 | 2021 | 27 | |
| 5 | 2023 | 25 | |
| 6 | 2020 | 23 | |
| 7 | 2018 | 21 | |
| 8 | 2012 | 19 | |
| 9 | 2019 | 19 | |
| 10 | 2022 | 14 | |
| 11 | 2021 | 13 | |
| 12 | 2019 | 5 | |
| 13 | 2002 | 4 | |
| 14 | 2024 | 4 | |
| 15 | 2018 | 2 | |
| 16 | 2014 | 2 | |
| 17 | 2018 | 1 | |
| 18 | 2014 | 1 | |
| 19 | 2024 | 0 | |
| 20 | 2025 | 0 |
About Dan Jiang
Dan Jiang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Information Systems and Hardware and Architecture, having authored 20 papers that have together received 299 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (4 papers), Complex Network Analysis Techniques (4 papers), Recommender Systems and Techniques (3 papers), Topic Modeling (3 papers), Image Retrieval and Classification Techniques (2 papers), Advanced Algorithms and Applications (2 papers), Advancements in Photolithography Techniques (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (53 citations), Artificial Intelligence (129 citations), Statistical and Nonlinear Physics (41 citations), Computational Mathematics (2 citations) and Hardware and Architecture (13 citations). Dan Jiang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Nagarajan Raghavan, Rongbin Xu, Lixia Xue, Ronggui Wang, Xinmei Wang, Ying Xie, Ying Xie, Juan Yang, Xin Hua Xu and Jie Zhou. Their work appears in journals such as IEEE Access, Information Sciences, Knowledge-Based Systems, Expert Systems with Applications and Physics of Plasmas.
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.