Pu Wu
Impact in
- Geometry and Topology top 5%
- Graph theory and applications
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- Computational Drug Discovery Methods
- Graph Labeling and Dimension Problems
- Advanced Graph Theory Research
- Complexity and Algorithms in Graphs
Papers in
-
- Advanced Graph Theory Research 15
- Complexity and Algorithms in Graphs 12
- Graph Labeling and Dimension Problems 7
- Computational Drug Discovery Methods 3
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- Interconnection Networks and Systems 4
- Optimization and Search Problems 3
- Co-authors
- Zehui Shao (24 shared papers)Xiujun Zhang (3 shared papers)Jia‐Bao Liu (5 shared papers)Darko Dimitrov (1 shared paper)İvan Gutman (1 shared paper)Yingying Gao (1 shared paper)Huiqin Jiang (11 shared papers)Seyed Mahmoud Sheikholeslami (7 shared papers)
In The Last Decade
Pu Wu
24 papers receiving 368 citations
Peers
Comparison fields: 5 of 55
- Geometry and Topology 214
- Computational Theory and Mathematics 303
- Discrete Mathematics and Combinatorics 27
- Organic Chemistry 119
- Management Science and Operations Research 21
Countries citing papers authored by Pu Wu
This map shows the geographic impact of Pu Wu'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 Pu Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pu Wu more than expected).
Fields of papers citing papers by Pu Wu
This network shows the impact of papers produced by Pu Wu. 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 Pu Wu. The network helps show where Pu Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Pu Wu, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 95 | |
| 2 | 2017 | 92 | |
| 3 | 2018 | 28 | |
| 4 | 2020 | 26 | |
| 5 | 2018 | 20 | |
| 6 | 2018 | 17 | |
| 7 | 2018 | 16 | |
| 8 | 2020 | 15 | |
| 9 | 2017 | 9 | |
| 10 | 2018 | 8 | |
| 11 | 2018 | 8 | |
| 12 | 2019 | 7 | |
| 13 | 2018 | 6 | |
| 14 | 2021 | 5 | |
| 15 | 2018 | 4 | |
| 16 | 2018 | 4 | |
| 17 | 2018 | 3 | |
| 18 | 2021 | 3 | |
| 19 | 2021 | 3 | |
| 20 | 2018 | 2 |
About Pu Wu
Pu Wu is a scholar working on Computational Theory and Mathematics, Computer Networks and Communications, Geometry and Topology, Organic Chemistry and Discrete Mathematics and Combinatorics, having authored 25 papers that have together received 375 indexed citations. Recurring topics across this work include Advanced Graph Theory Research (15 papers), Complexity and Algorithms in Graphs (12 papers), Graph Labeling and Dimension Problems (7 papers), Graph theory and applications (6 papers), Interconnection Networks and Systems (4 papers), Optimization and Search Problems (3 papers), Computational Drug Discovery Methods (3 papers) and Synthesis and Properties of Aromatic Compounds (3 papers). The work is most often cited by research in Geometry and Topology (214 citations), Computational Theory and Mathematics (303 citations), Discrete Mathematics and Combinatorics (27 citations), Organic Chemistry (119 citations) and Management Science and Operations Research (21 citations). Pu Wu has collaborated with scholars based in China, Iran and Slovenia. Frequent co-authors include Zehui Shao, Xiujun Zhang, Jia‐Bao Liu, Darko Dimitrov, İvan Gutman, Yingying Gao, Huiqin Jiang, Seyed Mahmoud Sheikholeslami, Zepeng Li and Janez Žerovnik. Their work appears in journals such as IEEE Access, Discrete Applied Mathematics, Symmetry, Journal of Intelligent & Fuzzy Systems and Journal of Combinatorial Optimization.
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.