Jinhua Gao
- Polymers and Plastics top 0.2%
- Conducting polymers and applications 35
-
- Organic Electronics and Photovoltaics 41
- Perovskite Materials and Applications 30
- Thin-Film Transistor Technologies 11
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- Complex Network Analysis Techniques 10
- Opinion Dynamics and Social Influence 6
- Materials Chemistry top 10%
- Artificial Intelligence top 5%
- Topic Modeling 7
- Advanced Graph Neural Networks 6
- Cited by
- Polymers and PlasticsElectrical and Electronic EngineeringStatistical and Nonlinear Physics
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Jinhua Gao
85 papers receiving 4.9k citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Polymers and Plastics 3.5k
- Electrical and Electronic Engineering 4.3k
- Statistical and Nonlinear Physics 181
- Materials Chemistry 527
- Artificial Intelligence 234
Countries citing papers authored by Jinhua Gao
This map shows the geographic impact of Jinhua Gao'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 Jinhua Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinhua Gao more than expected).
Fields of papers citing papers by Jinhua Gao
This network shows the impact of papers produced by Jinhua Gao. 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 Jinhua Gao. The network helps show where Jinhua Gao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jinhua Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 10 | |
| 5 | 2023 | 11 | |
| 6 | 2023 | 13 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2021 | 81 | |
| 10 | 2021 | 126 | |
| 11 | 2021 | 1 | |
| 12 | 2020 | 211 | |
| 13 | 2020 | 261 | |
| 14 | 2020 | 5 | |
| 15 | Coupled Graph Neural Networks for Predicting the Popularity of Online Content. | 2019 | 2 |
| 16 | 2019 | 15 | |
| 17 | 2019 | 250 | |
| 18 | ICTNET at Microblog Track TREC 2011 | 2011 | 4 |
| 19 | 2011 | 2 | |
| 20 | 1999 | 48 |
About Jinhua Gao
Jinhua Gao is a scholar working on Polymers and Plastics, Electrical and Electronic Engineering and Statistical and Nonlinear Physics, having authored 89 papers that have together received 5.0k indexed citations. Recurring topics across this work include Organic Electronics and Photovoltaics (41 papers), Conducting polymers and applications (35 papers), Perovskite Materials and Applications (30 papers), Thin-Film Transistor Technologies (11 papers), Complex Network Analysis Techniques (10 papers), Topic Modeling (7 papers), Advanced Graph Neural Networks (6 papers) and Opinion Dynamics and Social Influence (6 papers). The work is most often cited by research in Polymers and Plastics (3.5k citations), Electrical and Electronic Engineering (4.3k citations) and Statistical and Nonlinear Physics (181 citations). Jinhua Gao has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Xiaoling Ma, Fujun Zhang, Chunyu Xu, Zhenghao Hu, Qiaoshi An, Jian Wang, Xiaoli Zhang, Jian Zhang, Chuluo Yang and Wei Gao. Their work appears in journals such as Surface Science, Physical review. B., Solar RRL, Nano Energy and Journal of Materials Chemistry A.
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.