Xiaojun Ye

2.2k citations
154 papers · 1.3k · h-index 19

Impact in

Papers in

Xiaojun Ye

139 papers receiving 1.3k citations

Peers

Xiaojun Ye
Comparison fields: 5 of 147
  • Artificial Intelligence 281
  • Computer Vision and Pattern Recognition 148
  • Statistical and Nonlinear Physics 89
  • Software 26
  • Signal Processing 69
Replace Xiangjun Li with:
Xiangjun Li China
Yanxiang He China
Wenyuan Liu China
Anupam Yadav India
Hong Xie China
Qingxian Wang China
Qing Zhang China
Bhavya Kailkhura United States
Xiaojun Ye relative to Xiangjun Li China Xiangjun Li's profile →
Citations per field
00.5×20×40×54×
Xiangjun Li · 1×
Citations per year

Countries citing papers authored by Xiaojun Ye

Since Specialization
Citations

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

Fields of papers citing papers by Xiaojun Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 154 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201371
2 201769
3 201861
4 201758
5 202254
6 202154
7 202144
8 202040
9 202338
10 202233
11 201832
12 202028
13 201622
14 201921
15 201820
16 201919
17 202019
18 202119
19 202219
20 201718

About Xiaojun Ye

Xiaojun Ye is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Materials Chemistry, Information Systems and Computer Networks and Communications, having authored 154 papers that have together received 1.3k indexed citations. Recurring topics across this work include Silicon and Solar Cell Technologies (14 papers), Privacy-Preserving Technologies in Data (11 papers), 2D Materials and Applications (10 papers), Internet Traffic Analysis and Secure E-voting (9 papers), Complex Network Analysis Techniques (9 papers), Cryptography and Data Security (8 papers), Service-Oriented Architecture and Web Services (8 papers) and Semiconductor materials and devices (8 papers). The work is most often cited by research in Artificial Intelligence (281 citations), Computer Vision and Pattern Recognition (148 citations), Statistical and Nonlinear Physics (89 citations), Software (26 citations) and Signal Processing (69 citations). Xiaojun Ye has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Chaokun Wang, Hongbo Li, Rui Zhang, Zheng Wang, Jianmin Wang, Jing Yu, Qiang Tu, Xiao Yuan, Hongbo Li and Changping Wang. Their work appears in journals such as Journal of Materials Science Materials in Electronics, Materials Science in Semiconductor Processing, Journal of Physics and Chemistry of Solids, Tsinghua Science & Technology and RSC Advances.

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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