Depeng Dang
Impact in
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 10%
- Topic Modeling
- Advanced Graph Neural Networks
- Natural Language Processing Techniques
Papers in
-
- Mobile Crowdsensing and Crowdsourcing 7
-
- Image and Signal Denoising Methods 8
- Advanced Image Processing Techniques 8
- Co-authors
- Xue JiangYanjing SuTurab LookmanJianxin XieYing LiuJingfan YangNing WangXingjian Wang
- Journals
- Information Sciences (3 papers)IEEE Access (3 papers)Pattern Recognition (3 papers)IEEE Transactions on Circuits and Systems for Video Technology (2 papers)IEEE Geoscience and Remote Sensing Letters (2 papers)
- Partner nations
- ChinaUnited KingdomSlovenia
In The Last Decade
Depeng Dang
36 papers receiving 279 citations
Peers
Comparison fields: 5 of 80
- Computer Science Applications 25
- Artificial Intelligence 119
- Metals and Alloys 7
- Information Systems 60
- Computer Vision and Pattern Recognition 53
Countries citing papers authored by Depeng Dang
This map shows the geographic impact of Depeng Dang'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 Depeng Dang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Depeng Dang more than expected).
Fields of papers citing papers by Depeng Dang
This network shows the impact of papers produced by Depeng Dang. 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 Depeng Dang. The network helps show where Depeng Dang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Depeng Dang, 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 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 4 | |
| 8 | 2024 | 12 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 5 | |
| 12 | 2024 | 1 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 7 | |
| 15 | 2022 | 19 | |
| 16 | 2021 | 14 | |
| 17 | 2020 | 14 | |
| 18 | 2019 | 2 | |
| 19 | Urban emergency capability assessment system based on grey analytic hierarchy process | 2012 | 1 |
| 20 | 2003 | 1 |
About Depeng Dang
Depeng Dang is a scholar working on Computer Science Applications, Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology and Information Systems, having authored 41 papers that have together received 292 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Image and Signal Denoising Methods (8 papers), Advanced Image Processing Techniques (8 papers), Mobile Crowdsensing and Crowdsourcing (7 papers), Natural Language Processing Techniques (7 papers), Advanced Graph Neural Networks (6 papers), Web Data Mining and Analysis (4 papers) and Recommender Systems and Techniques (4 papers). The work is most often cited by research in Computer Science Applications (25 citations), Artificial Intelligence (119 citations), Metals and Alloys (7 citations), Information Systems (60 citations) and Computer Vision and Pattern Recognition (53 citations). Depeng Dang has collaborated with scholars based in China, United Kingdom and Slovenia. Frequent co-authors include Xue Jiang, Yanjing Su, Turab Lookman, Jianxin Xie, Ying Liu, Jingfan Yang, Ning Wang, Xingjian Wang, Zhihui Li and Ning Wang. Their work appears in journals such as Information Sciences, IEEE Access, Pattern Recognition, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Geoscience and Remote Sensing Letters.
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.