Gai Li

751 citations
31 papers · 564 · h-index 15

Impact in

Papers in

Gai Li

29 papers receiving 546 citations

Peers

Gai Li
Comparison fields: 5 of 87
  • Renewable Energy, Sustainability and the Environment 90
  • Information Systems 113
  • Computer Vision and Pattern Recognition 93
  • Energy Engineering and Power Technology 11
  • Automotive Engineering 41
Replace Xiaoyong Zhang with:
Xiaoyong Zhang China
Zhi Cai China
Yajuan Zhang China
Tian Guo China
Vikas Kumar India
Xiaochen Zhang China
Dongkyu Choi United States
Tingting He China
Gai Li relative to Xiaoyong Zhang China Xiaoyong Zhang's profile →
Citations per field
00.5×2.6×
Xiaoyong Zhang · 1×
Citations per year

Countries citing papers authored by Gai Li

Since Specialization
Citations

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

Fields of papers citing papers by Gai Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201670
2 201652
3 201649
4 201146
5 201739
6 201638
7 201534
8 201629
9 202422
10 201719
11 202418
12 201418
13 201118
14 201616
15 201416
16 201514
17 202511
18 202211
19 20179
20 20118

About Gai Li

Gai Li is a scholar working on Information Systems, Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition and Renewable Energy, Sustainability and the Environment, having authored 31 papers that have together received 564 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (8 papers), Expert finding and Q&A systems (4 papers), Face and Expression Recognition (3 papers), Electrocatalysts for Energy Conversion (3 papers), Advanced battery technologies research (3 papers), Fuel Cells and Related Materials (3 papers), Conducting polymers and applications (2 papers) and Text and Document Classification Technologies (2 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (90 citations), Information Systems (113 citations), Computer Vision and Pattern Recognition (93 citations), Energy Engineering and Power Technology (11 citations) and Automotive Engineering (41 citations). Gai Li has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Qiang Chen, Weihua Ou, Danli Zeng, Yubao Sun, Hansong Cheng, Liyang Wang, Jian Lü, Shujian Yu, Yian Zhu and Shengyi Zhang. Their work appears in journals such as Chemical Communications, Neurocomputing, Nano Energy, Scientific Reports and Applied Surface Science.

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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