Wei Min Wang

15 papers receiving 432 citations

Wei Min Wang's Hit Papers

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships 2016 · 385 citations
3850+3+6Years since publication100200300

Peers

Wei Min Wang
Comparison fields: 5 of 144
  • Computational Theory and Mathematics 150
  • Industrial and Manufacturing Engineering 21
  • Environmental Engineering 27
  • Management of Technology and Innovation 13
  • Analytical Chemistry 18
Replace Belmiro P.M. Duarte with:
Belmiro P.M. Duarte Portugal
Xinya Wang China
Zhan‐Heng Chen China
Pradeep Suresh United States
Feng China
Xiangqian Feng China
Ye Yang China
Alexei Botchkarev Canada
Wen Chen China
Wei Min Wang relative to Belmiro P.M. Duarte Portugal Belmiro P.M. Duarte's profile →
Citations per field
00.5×1.5×2.0×
Belmiro P.M. Duarte · 1×
Citations per year

Countries citing papers authored by Wei Min Wang

Since Specialization
Citations

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

Fields of papers citing papers by Wei Min Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Wei Min 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 Wei Min Wang Line = papers co-authored together Wei Min Wang links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
Hit paper breakdown →
2016385
2 201710
3 201610
4 20218
5 20157
6 20247
7 20115
8 20244
9 20183
10 20192
11 20241
12 20181
13 20231
14 20201
15 20131
16 20240
17 20240

About Wei Min Wang

Wei Min Wang is a scholar working on Artificial Intelligence, Mechanical Engineering, Strategy and Management, Management of Technology and Innovation and Management Information Systems, having authored 17 papers that have together received 446 indexed citations. Recurring topics across this work include Design Education and Practice (5 papers), Product Development and Customization (2 papers), Semantic Web and Ontologies (2 papers), Information Systems Theories and Implementation (2 papers), Service and Product Innovation (2 papers), Business Process Modeling and Analysis (2 papers), Complex Systems and Decision Making (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Computational Theory and Mathematics (150 citations), Industrial and Manufacturing Engineering (21 citations), Environmental Engineering (27 citations), Management of Technology and Innovation (13 citations) and Analytical Chemistry (18 citations). Wei Min Wang has collaborated with scholars based in China, Germany and United States. Frequent co-authors include Eric Gifford, Junshui Ma, Robert P. Sheridan, Andy Liaw, Rainer Stark, Henrique Rozenfeld, Kai Lindow, Cheng Yang, Dandan Wu and Roy McCann. Their work appears in journals such as ISPRS International Journal of Geo-Information, Research in Engineering Design, Sensors, Journal of Industrial and Management Optimization and Journal of Chemical Information and Modeling.

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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