Junli Li
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- Complex Network Analysis Techniques 12
- Opinion Dynamics and Social Influence 3
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- Image and Signal Denoising Methods 2
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks 4
- Evolutionary Algorithms and Applications 3
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- Network Security and Intrusion Detection 9
- Software-Defined Networks and 5G 7
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- Advanced Memory and Neural Computing 3
Junli Li
30 papers receiving 379 citations
Peers
Comparison fields: 5 of 84
- Computer Science Applications 55
- Statistical and Nonlinear Physics 59
- Computer Vision and Pattern Recognition 92
- Artificial Intelligence 133
- Computer Networks and Communications 88
Countries citing papers authored by Junli Li
This map shows the geographic impact of Junli Li'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 Junli Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junli Li more than expected).
Fields of papers citing papers by Junli Li
This network shows the impact of papers produced by Junli Li. 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 Junli Li. The network helps show where Junli Li may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Junli Li, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 9 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 2 | |
| 7 | 2023 | 10 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2023 | 17 | |
| 13 | 2023 | 1 | |
| 14 | 2022 | 27 | |
| 15 | 2022 | 46 | |
| 16 | 2022 | 4 | |
| 17 | 2022 | 19 | |
| 18 | 2022 | 19 | |
| 19 | A differential evolution algorithm based on individual-sorting and individual-sampling strategies | 2012 | 4 |
| 20 | A CoEvolutionary algorithm based on Elitism and Gravitational Evolution strategies | 2012 | 4 |
About Junli Li
Junli Li is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 32 papers that have together received 404 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (12 papers), Network Security and Intrusion Detection (9 papers), Software-Defined Networks and 5G (7 papers), Advanced Graph Neural Networks (4 papers), Advanced Memory and Neural Computing (3 papers), Evolutionary Algorithms and Applications (3 papers), Opinion Dynamics and Social Influence (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Computer Science Applications (55 citations), Statistical and Nonlinear Physics (59 citations), Computer Vision and Pattern Recognition (92 citations), Artificial Intelligence (133 citations) and Computer Networks and Communications (88 citations). Junli Li has collaborated with scholars based in China, Hong Kong and Taiwan. Frequent co-authors include Bingqian Jiang, Minjuan Wang, Yang Lou, Lin Wang, Guanrong Chen, Xiang Li, Guofeng Xia, Liwei Yang, Chen Gang and Wei Ping. Their work appears in journals such as Information Sciences, IEEE Transactions on Cybernetics, Journal of Computer Information Systems, Electronics and IEEE Journal of Biomedical and Health Informatics.
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.