Zhigao Guo
- Artificial Intelligence top 10%
- Management Science and Operations Research top 10%
- Information Systems
- Computational Theory and Mathematics
- Statistics, Probability and Uncertainty top 10%
- Co-authors
- Anthony C. ConstantinouNeville K. KitsonYang LiuXiaoguang GaoDaqing ChenYang YuHao RenKaifang Wan
- Topics
- Bayesian Modeling and Causal Inference (12 papers)Rough Sets and Fuzzy Logic (3 papers)Data Quality and Management (3 papers)
- Cited by
- Artificial IntelligenceStatistics, Probability and UncertaintyManagement Science and Operations Research
- Journals
- Pattern RecognitionArtificial Intelligence ReviewInternational Journal of Approximate Reasoning
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Zhigao Guo
16 papers receiving 263 citations
Hit Papers
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 149
- Management Science and Operations Research 45
- Information Systems 32
- Computational Theory and Mathematics 31
- Statistics, Probability and Uncertainty 30
Countries citing papers authored by Zhigao Guo
This map shows the geographic impact of Zhigao Guo'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 Zhigao Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhigao Guo more than expected).
Fields of papers citing papers by Zhigao Guo
This network shows the impact of papers produced by Zhigao Guo. 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 Zhigao Guo. The network helps show where Zhigao Guo may publish in the future.
Co-authorship network of co-authors of Zhigao Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Zhigao Guo. A scholar is included among the top collaborators of Zhigao Guo 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 Zhigao Guo. Zhigao Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | A survey of Bayesian Network structure learningbreakdown → | 143 |
| 3 | 13 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 26 | |
| 8 | 24 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 12 | |
| 12 | 3 | |
| 13 | 22 | |
| 14 | Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data. | 1 |
| 15 | 3 | |
| 16 | UCAV Targets Assignment Method Based on Uncertain Information | 2 |
About Zhigao Guo
Zhigao Guo is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics, having authored 16 papers that have together received 278 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (12 papers), Rough Sets and Fuzzy Logic (3 papers) and Data Quality and Management (3 papers). The work is most often cited by research in Artificial Intelligence (149 citations), Statistics, Probability and Uncertainty (30 citations) and Management Science and Operations Research (45 citations). Zhigao Guo has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Anthony C. Constantinou, Neville K. Kitson, Yang Liu, Xiaoguang Gao, Daqing Chen, Yang Yu, Hao Ren, Kaifang Wan, Xiaoguang Gao and Yu Yang. Their work appears in journals such as Pattern Recognition, Artificial Intelligence Review and International Journal of Approximate Reasoning.
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.