Bin Ge

105 papers receiving 1.8k citations

Peers

Bin Ge
Comparison fields: 5 of 94
  • Developmental Neuroscience 150
  • Emergency Medical Services 111
  • General Health Professions 234
  • Health Information Management 41
  • Speech and Hearing 23
Replace Khae Hawn Kim with:
Khae Hawn Kim South Korea
Hee Young Hwang South Korea
Ferdinando di Orio Italy
Daniel Grandt Germany
Evan D. Muse United States
Hamed Tabesh Iran
Willy Chou Taiwan
Robert R. Bies United States
John F. Hurdle United States
Kazuki Ide Japan
Bin Ge relative to Khae Hawn Kim South Korea Khae Hawn Kim's profile →
Citations per field
00.5×5×10×15×18.5×
Khae Hawn Kim · 1×
Citations per year

Countries citing papers authored by Bin Ge

Since Specialization
Citations

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

Fields of papers citing papers by Bin Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 110 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999186
2 2005162
3 2018100
4 201097
5 201197
6 201781
7 200476
8 201076
9 201374
10 199768
11 200063
12 201642
13 202041
14 200836
15 201235
16 200732
17 200929
18 201829
19 201728
20 201427

About Bin Ge

Bin Ge is a scholar working on General Health Professions, Computer Networks and Communications, Artificial Intelligence, Developmental Neuroscience and Public Health, Environmental and Occupational Health, having authored 110 papers that have together received 1.9k indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Geriatric Care and Nursing Homes (5 papers), Health Literacy and Information Accessibility (5 papers), Neurogenesis and neuroplasticity mechanisms (4 papers), Smoking Behavior and Cessation (3 papers), Rheumatoid Arthritis Research and Therapies (3 papers), Robotics and Sensor-Based Localization (3 papers) and Complex Network Analysis Techniques (3 papers). The work is most often cited by research in Developmental Neuroscience (150 citations), Emergency Medical Services (111 citations), General Health Professions (234 citations), Health Information Management (41 citations) and Speech and Hearing (23 citations). Bin Ge has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Ian D. Duncan, Su‐Chun Zhang, John E. Hewett, Daniel R. Longo, Shari Schubert, Robin L. Kruse, Cheryl L. Shigaki, Greg F. Petroski, Karen L. Smarr and Weidong Xiao. Their work appears in journals such as Chronic Illness, IEEE Access, Applied Sciences, International Journal of Biomathematics and The Journal of Rural Health.

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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