Jun Liu
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- Multi-Criteria Decision Making 74
- Computational Theory and Mathematics top 0.2%
- Rough Sets and Fuzzy Logic 74
- Advanced Algebra and Logic 39
- Artificial Intelligence top 0.5%
- Logic, Reasoning, and Knowledge 36
- Bayesian Modeling and Causal Inference 27
- Fuzzy Logic and Control Systems 22
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- Risk and Safety Analysis 17
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- Context-Aware Activity Recognition Systems 19
- Cited by
- Management Science and Operations ResearchComputational Theory and MathematicsArtificial Intelligence
- Journals
- International Journal of Computational Intelligence Systems (20 papers)Information Sciences (18 papers)Knowledge-Based Systems (15 papers)
- Partner nations
- ChinaUnited KingdomSpain
In The Last Decade
Jun Liu
385 papers receiving 6.4k citations
Hit Papers
Peers
Comparison fields: 5 of 198
- Management Science and Operations Research 2.0k
- Computational Theory and Mathematics 1.4k
- Artificial Intelligence 2.1k
- Statistics, Probability and Uncertainty 395
- Organizational Behavior and Human Resource Management 507
Countries citing papers authored by Jun Liu
This map shows the geographic impact of Jun Liu'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 Jun Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Liu more than expected).
Fields of papers citing papers by Jun Liu
This network shows the impact of papers produced by Jun Liu. 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 Jun Liu. The network helps show where Jun Liu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Liu, 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 | 3 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 7 | |
| 4 | 2024 | 11 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 0 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 14 | |
| 12 | 2023 | 17 | |
| 13 | 2023 | 4 | |
| 14 | 2023 | 13 | |
| 15 | 2023 | 0 | |
| 16 | 2022 | 1 | |
| 17 | 2019 | 29 | |
| 18 | 2019 | 0 | |
| 19 | 2018 | 8 | |
| 20 | 2007 | 3 |
About Jun Liu
Jun Liu is a scholar working on Computational Theory and Mathematics, Management Science and Operations Research, Artificial Intelligence, Statistics, Probability and Uncertainty and Information Systems, having authored 455 papers that have together received 6.7k indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (74 papers), Rough Sets and Fuzzy Logic (74 papers), Advanced Algebra and Logic (39 papers), Logic, Reasoning, and Knowledge (36 papers), Bayesian Modeling and Causal Inference (27 papers), Fuzzy Logic and Control Systems (22 papers), Context-Aware Activity Recognition Systems (19 papers) and Risk and Safety Analysis (17 papers). The work is most often cited by research in Management Science and Operations Research (2.0k citations), Computational Theory and Mathematics (1.4k citations), Artificial Intelligence (2.1k citations), Statistics, Probability and Uncertainty (395 citations) and Organizational Behavior and Human Resource Management (507 citations). Jun Liu has collaborated with scholars based in China, United Kingdom and Spain. Frequent co-authors include Luis Martı́nez, Jianbo Yang, Hui Wang, Hongwei Wang, Da Ruan, Jin Wang, H. S. Sii, Randall Sadler, Jian-Bo Yang and Dong‐Ling Xu. Their work appears in journals such as International Journal of Computational Intelligence Systems, Information Sciences, Knowledge-Based Systems, Soft Computing and Applied Soft Computing.
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.