Longlong Li
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Industrial and Manufacturing Engineering top 10%
- Organic Chemistry
- Signal Processing
- Co-authors
- Zhifeng WangXuehu YanTingting ZhangYuliang LuLintao LiuDayun HuangGuobing YanChunyan Zeng
- Topics
- Advanced Steganography and Watermarking Techniques (21 papers)Chaos-based Image/Signal Encryption (16 papers)Cryptography and Data Security (9 papers)
- Cited by
- Computer Vision and Pattern RecognitionIndustrial and Manufacturing EngineeringArtificial Intelligence
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Longlong Li
39 papers receiving 319 citations
Peers
Comparison fields: 5 of 69
- Computer Vision and Pattern Recognition 176
- Artificial Intelligence 106
- Industrial and Manufacturing Engineering 39
- Organic Chemistry 30
- Signal Processing 27
Countries citing papers authored by Longlong Li
This map shows the geographic impact of Longlong 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 Longlong Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Longlong Li more than expected).
Fields of papers citing papers by Longlong Li
This network shows the impact of papers produced by Longlong 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 Longlong Li. The network helps show where Longlong Li may publish in the future.
Co-authorship network of co-authors of Longlong Li
This figure shows the co-authorship network connecting the top 25 collaborators of Longlong Li. A scholar is included among the top collaborators of Longlong Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Longlong Li. Longlong Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 21 | |
| 8 | 2 | |
| 9 | 7 | |
| 10 | 81 | |
| 11 | 13 | |
| 12 | 4 | |
| 13 | 11 | |
| 14 | 6 | |
| 15 | 2 | |
| 16 | 2 | |
| 17 | 9 | |
| 18 | 0 | |
| 19 | 3 | |
| 20 | 1 |
About Longlong Li
Longlong Li is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 43 papers that have together received 324 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (21 papers), Chaos-based Image/Signal Encryption (16 papers) and Cryptography and Data Security (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (176 citations), Industrial and Manufacturing Engineering (39 citations) and Artificial Intelligence (106 citations). Longlong Li has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Zhifeng Wang, Xuehu Yan, Tingting Zhang, Yuliang Lu, Lintao Liu, Dayun Huang, Guobing Yan, Chunyan Zeng, Jingju Liu and Liangjun Li. Their work appears in journals such as Chemical Engineering Journal, ACS Applied Materials & Interfaces and IEEE Access.
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.