Bei Wang

96 papers receiving 1.0k citations

Peers

Bei Wang
Comparison fields: 5 of 130
  • Computer Graphics and Computer-Aided Design 93
  • Computer Vision and Pattern Recognition 520
  • Computational Theory and Mathematics 376
  • Biophysics 133
  • Computational Mathematics 6
Replace Olivier Lézoray with:
Olivier Lézoray France
Dmitriy Morozov United States
Xiamu Niu China
Beijing Chen China
I. García Spain
Yusu Wang United States
Carrie Grimes United States
Gunther H. Weber United States
Xin Jin China
Frédéric Chazal France
Bei Wang relative to Olivier Lézoray France Olivier Lézoray's profile →
Citations per field
00.5×3.3×
Olivier Lézoray · 1×
Citations per year

Countries citing papers authored by Bei Wang

Since Specialization
Citations

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

Fields of papers citing papers by Bei Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016264
2 201768
3 202144
4 201932
5 201830
6 201129
7 201325
8 201824
9 201523
10 202021
11 200921
12 201921
13 201421
14 202120
15 201518
16 201117
17 201215
18 201515
19 202013
20 202013

About Bei Wang

Bei Wang is a scholar working on Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Artificial Intelligence, Biophysics and Information Systems, having authored 111 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topological and Geometric Data Analysis (57 papers), Data Visualization and Analytics (35 papers), Cell Image Analysis Techniques (14 papers), Computer Graphics and Visualization Techniques (9 papers), Complex Network Analysis Techniques (8 papers), Data Management and Algorithms (7 papers), Digital Image Processing Techniques (6 papers) and Software Engineering Research (5 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (93 citations), Computer Vision and Pattern Recognition (520 citations), Computational Theory and Mathematics (376 citations), Biophysics (133 citations) and Computational Mathematics (6 citations). Bei Wang has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Valerio Pascucci, Peer‐Timo Bremer, Shusen Liu, Dan Maljovec, Paul Rosen, Lin Yan, Ingrid Hotz, Mustafa Hajij, Vivek Srikumar and Carlos Scheidegger. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Computational Geometry, Discrete & Computational Geometry and Nature Cell Biology.

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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