Guan Jun Wang

721 citations
28 papers · 592 · h-index 14

Impact in

Papers in

Guan Jun Wang

27 papers receiving 570 citations

Peers

Guan Jun Wang
Comparison fields: 5 of 47
  • Software 294
  • Safety, Risk, Reliability and Quality 538
  • Statistics and Probability 355
  • Statistics, Probability and Uncertainty 163
  • Management Information Systems 59
Replace Delia Montoro‐Cazorla with:
Delia Montoro‐Cazorla Spain
Francisco Germán Badía Spain
William Murdock United States
Wagner de Souza Borges Brazil
R. Guo South Africa
Yingsai Cao China
Chang Hoon Lie South Korea
Xujie Jia China
Linmin Hu China
Jessica Yuan Taiwan
Guan Jun Wang relative to Delia Montoro‐Cazorla Spain Delia Montoro‐Cazorla's profile →
Citations per field
00.5×1.5×2×2.5×
Delia Montoro‐Cazorla · 1×
Citations per year

Countries citing papers authored by Guan Jun Wang

Since Specialization
Citations

This map shows the geographic impact of Guan Jun Wang'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 Guan Jun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guan Jun Wang more than expected).

Fields of papers citing papers by Guan Jun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guan Jun Wang. 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 Guan Jun Wang. The network helps show where Guan Jun Wang may publish in the future.

Co-authors

The 6 scholars most cited alongside Guan Jun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Guan Jun Wang Line = papers co-authored together Guan Jun Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200599
2 200679
3 200949
4 201237
5 201432
6 200627
7 200625
8 200724
9 201723
10 200822
11 201121
12 200919
13 201518
14 201616
15 201014
16 201613
17 201113
18 201412
19 201012
20 200411

About Guan Jun Wang

Guan Jun Wang is a scholar working on Software, Safety, Risk, Reliability and Quality, Statistics and Probability, Statistics, Probability and Uncertainty and Accounting, having authored 28 papers that have together received 592 indexed citations. Recurring topics across this work include Reliability and Maintenance Optimization (23 papers), Statistical Distribution Estimation and Applications (21 papers), Software Reliability and Analysis Research (20 papers), Financial Reporting and Valuation Research (2 papers), Corporate Taxation and Avoidance (2 papers), Probabilistic and Robust Engineering Design (2 papers), Risk and Safety Analysis (2 papers) and Fiscal Policy and Economic Growth (2 papers). The work is most often cited by research in Software (294 citations), Safety, Risk, Reliability and Quality (538 citations), Statistics and Probability (355 citations), Statistics, Probability and Uncertainty (163 citations) and Management Information Systems (59 citations). Guan Jun Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yuan Lin Zhang, Richard C.M. Yam, Rui Peng, Jacques Beaumont, Qiang Li and Bo Qian. Their work appears in journals such as Applied Mathematical Modelling, International Journal of Systems Science, Applied Mathematics and Computation, International Journal of Systems Science Operations & Logistics and European Journal of Operational Research.

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.

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