Lujia Pan
Impact in
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
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- Advanced Graph Neural Networks 5
- Anomaly Detection Techniques and Applications 3
- Machine Learning and Algorithms 2
- Imbalanced Data Classification Techniques 2
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- Caching and Content Delivery 4
- Software System Performance and Reliability 2
- Distributed systems and fault tolerance 2
- Co-authors
- Pengyun Wang (3 shared papers)Hong Cheng (1 shared paper)Jia Li (1 shared paper)Jianfeng Zhang (1 shared paper)Zhichao Han (1 shared paper)Patrick P. C. Lee (3 shared papers)Min Zhou (5 shared papers)Qi Li (1 shared paper)
In The Last Decade
Lujia Pan
22 papers receiving 223 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 137
- Computational Mathematics 2
- Signal Processing 28
- Building and Construction 30
- Transportation 14
Countries citing papers authored by Lujia Pan
This map shows the geographic impact of Lujia Pan'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 Lujia Pan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lujia Pan more than expected).
Fields of papers citing papers by Lujia Pan
This network shows the impact of papers produced by Lujia Pan. 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 Lujia Pan. The network helps show where Lujia Pan may publish in the future.
Co-authors
The 25 scholars most cited alongside Lujia Pan, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 76 | |
| 2 | 2018 | 24 | |
| 3 | 2022 | 23 | |
| 4 | 2022 | 15 | |
| 5 | 2021 | 14 | |
| 6 | 2019 | 14 | |
| 7 | 2020 | 11 | |
| 8 | 2015 | 8 | |
| 9 | 2023 | 6 | |
| 10 | 2023 | 6 | |
| 11 | 2014 | 6 | |
| 12 | 2023 | 5 | |
| 13 | 2017 | 5 | |
| 14 | 2024 | 3 | |
| 15 | 2023 | 3 | |
| 16 | 2013 | 2 | |
| 17 | 2015 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2022 | 1 |
About Lujia Pan
Lujia Pan is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Computer Vision and Pattern Recognition and Information Systems, having authored 22 papers that have together received 229 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (5 papers), Time Series Analysis and Forecasting (5 papers), Caching and Content Delivery (4 papers), Anomaly Detection Techniques and Applications (3 papers), Software System Performance and Reliability (2 papers), Machine Learning and Algorithms (2 papers), Distributed systems and fault tolerance (2 papers) and Imbalanced Data Classification Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (137 citations), Computational Mathematics (2 citations), Signal Processing (28 citations), Building and Construction (30 citations) and Transportation (14 citations). Lujia Pan has collaborated with scholars based in China, Hong Kong and Sweden. Frequent co-authors include Pengyun Wang, Hong Cheng, Jia Li, Jianfeng Zhang, Zhichao Han, Patrick P. C. Lee, Min Zhou, Qi Li, Jun Wu and Meng‐Lin Yang. Their work appears in journals such as Proceedings of the VLDB Endowment, Computer Networks, IEEE Transactions on Neural Networks and Learning Systems, Naunyn-Schmiedeberg s Archives of Pharmacology and Journal of Systems and Software.
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.