Chongyang Shi
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Spam and Phishing Detection
- Artificial Intelligence top 5%
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Advanced Graph Neural Networks
Papers in
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- Topic Modeling 19
- Sentiment Analysis and Opinion Mining 11
- Advanced Text Analysis Techniques 6
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- Recommender Systems and Techniques 10
- Software Engineering Research 7
- Co-authors
- Zhendong Niu (10 shared papers)Longbing Cao (8 shared papers)Qi Zhang (9 shared papers)Sheng Huang (1 shared paper)An Lao (3 shared papers)Chuan-Ming Liu (4 shared papers)Xinyu Jiang (3 shared papers)Wei Chen (1 shared paper)
In The Last Decade
Chongyang Shi
45 papers receiving 486 citations
Peers
Comparison fields: 5 of 67
- Information Systems 245
- Artificial Intelligence 291
- Software 33
- Computational Mathematics 3
- Signal Processing 45
Countries citing papers authored by Chongyang Shi
This map shows the geographic impact of Chongyang Shi'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 Chongyang Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chongyang Shi more than expected).
Fields of papers citing papers by Chongyang Shi
This network shows the impact of papers produced by Chongyang Shi. 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 Chongyang Shi. The network helps show where Chongyang Shi may publish in the future.
Co-authors
The 25 scholars most cited alongside Chongyang Shi, 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 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 76 | |
| 2 | 2021 | 45 | |
| 3 | 2021 | 38 | |
| 4 | 2014 | 36 | |
| 5 | 2018 | 22 | |
| 6 | 2024 | 20 | |
| 7 | 2020 | 18 | |
| 8 | 2018 | 17 | |
| 9 | 2021 | 17 | |
| 10 | 2019 | 16 | |
| 11 | 2018 | 14 | |
| 12 | 2024 | 13 | |
| 13 | 2021 | 13 | |
| 14 | 2022 | 12 | |
| 15 | 2021 | 11 | |
| 16 | 2024 | 9 | |
| 17 | 2021 | 8 | |
| 18 | 2020 | 8 | |
| 19 | 2019 | 8 | |
| 20 | 2021 | 7 |
About Chongyang Shi
Chongyang Shi is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Software and Computer Vision and Pattern Recognition, having authored 50 papers that have together received 491 indexed citations. Recurring topics across this work include Topic Modeling (19 papers), Sentiment Analysis and Opinion Mining (11 papers), Recommender Systems and Techniques (10 papers), Software Engineering Research (7 papers), Advanced Text Analysis Techniques (6 papers), Misinformation and Its Impacts (5 papers), Rough Sets and Fuzzy Logic (5 papers) and Advanced Malware Detection Techniques (4 papers). The work is most often cited by research in Information Systems (245 citations), Artificial Intelligence (291 citations), Software (33 citations), Computational Mathematics (3 citations) and Signal Processing (45 citations). Chongyang Shi has collaborated with scholars based in China, Australia and Taiwan. Frequent co-authors include Zhendong Niu, Longbing Cao, Qi Zhang, Sheng Huang, An Lao, Chuan-Ming Liu, Xinyu Jiang, Wei Chen, Xiangyu Zhao and Ke Niu. Their work appears in journals such as Knowledge-Based Systems, Expert Systems with Applications, IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Software Engineering and Methodology and Engineering Applications of Artificial Intelligence.
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.