Jia Wu
Impact in
- Artificial Intelligence top 0.1%
- Advanced Graph Neural Networks
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Imbalanced Data Classification Techniques
- Text and Document Classification Technologies
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques
Papers in
-
- Advanced Graph Neural Networks 110
- Topic Modeling 36
- Anomaly Detection Techniques and Applications 21
- Domain Adaptation and Few-Shot Learning 20
Jia Wu
323 papers receiving 7.6k citations
Hit Papers
Peers
Comparison fields: 5 of 179
- Artificial Intelligence 4.5k
- Statistical and Nonlinear Physics 1.1k
- Computer Vision and Pattern Recognition 1.8k
- Media Technology 586
- Information Systems 1.5k
Countries citing papers authored by Jia Wu
This map shows the geographic impact of Jia Wu'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 Jia Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jia Wu more than expected).
Fields of papers citing papers by Jia Wu
This network shows the impact of papers produced by Jia Wu. 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 Jia Wu. The network helps show where Jia Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jia Wu, 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 | 5 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 38 | |
| 5 | 2024 | 17 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 8 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 2 | |
| 13 | 2023 | 4 | |
| 14 | 2023 | 23 | |
| 15 | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting Hit paper breakdown → | 2023 | 115 |
| 16 | 2023 | 17 | |
| 17 | 2023 | 9 | |
| 18 | 2023 | 4 | |
| 19 | 2021 | 6 | |
| 20 | 2020 | 45 |
About Jia Wu
Jia Wu is a scholar working on Artificial Intelligence, Computational Mathematics, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Information Systems, having authored 356 papers that have together received 7.8k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (110 papers), Complex Network Analysis Techniques (56 papers), Recommender Systems and Techniques (43 papers), Topic Modeling (36 papers), Anomaly Detection Techniques and Applications (21 papers), Domain Adaptation and Few-Shot Learning (20 papers), Time Series Analysis and Forecasting (20 papers) and Spam and Phishing Detection (19 papers). The work is most often cited by research in Artificial Intelligence (4.5k citations), Statistical and Nonlinear Physics (1.1k citations), Computer Vision and Pattern Recognition (1.8k citations), Media Technology (586 citations) and Information Systems (1.5k citations). Jia Wu has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Shirui Pan, Xingquan Zhu, Chengqi Zhang, Chuan Zhou, Zhihua Cai, Philip S. Yu, Jian Yang, Bo Du, Yongshan Zhang and Junjun Jiang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition and World Wide Web.
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.