Linwei Wang

50 papers receiving 829 citations

Peers

Linwei Wang
Comparison fields: 5 of 132
  • Infectious Diseases 157
  • Toxicology 26
  • Epidemiology 204
  • Modeling and Simulation 27
  • Cardiology and Cardiovascular Medicine 125
Replace Farrokh Habibzadeh with:
Farrokh Habibzadeh Iran
Qiushi Chen United States
Xiaosong Zhang China
Weina Li China
Azza Sarfraz Pakistan
Mahboobeh Yadollahie Iran
Jeruza Lavanholi Neyeloff Brazil
Jing Huang United States
Sheila M. Gore United Kingdom
Mark Holland United Kingdom
Linwei Wang relative to Farrokh Habibzadeh Iran Farrokh Habibzadeh's profile →
Citations per field
00.5×8.7×
Farrokh Habibzadeh · 1×
Citations per year

Countries citing papers authored by Linwei Wang

Since Specialization
Citations

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

Fields of papers citing papers by Linwei Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Linwei 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 Linwei Wang Line = papers co-authored together Linwei Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 60 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021120
2 201793
3 200984
4 202074
5 201144
6 201933
7 201628
8 202023
9 201923
10 202223
11 201721
12 201321
13 201520
14 201619
15 201617
16 202015
17 202114
18 202212
19 201911
20 20199

About Linwei Wang

Linwei Wang is a scholar working on Epidemiology, Infectious Diseases, Cardiology and Cardiovascular Medicine, General Health Professions and Sociology and Political Science, having authored 60 papers that have together received 838 indexed citations. Recurring topics across this work include HIV, Drug Use, Sexual Risk (16 papers), HIV/AIDS Research and Interventions (12 papers), Cardiac electrophysiology and arrhythmias (7 papers), Sex work and related issues (5 papers), Opioid Use Disorder Treatment (5 papers), Health disparities and outcomes (4 papers), ECG Monitoring and Analysis (4 papers) and COVID-19 Clinical Research Studies (4 papers). The work is most often cited by research in Infectious Diseases (157 citations), Toxicology (26 citations), Epidemiology (204 citations), Modeling and Simulation (27 citations) and Cardiology and Cardiovascular Medicine (125 citations). Linwei Wang has collaborated with scholars based in Canada, United States and China. Frequent co-authors include Pengcheng Shi, Huafeng Liu, Ken C. L. Wong, Rong Kong, Bohdan Nosyk, Zaid Ashiq Khan, Mansoor Ahmed Koondhar, Zhixiong Tan, Gazi Mahabubul Alam and Jeong Eun Min. Their work appears in journals such as American Journal of Epidemiology, Cancer, Clinical Infectious Diseases, CMAJ Open and Journal of the International AIDS Society.

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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