Xinli Yang

3.0k citations
80 papers · 2.4k · 1 hit paper · h-index 26

Impact in

Papers in

Xinli Yang

74 papers receiving 2.4k citations

Xinli Yang's Hit Papers

Deep Learning for Just-in-Time Defect Prediction 2015 · 238 citations
2380+3+7Years since publication50100150200

Peers

Xinli Yang
Comparison fields: 5 of 114
  • Software 370
  • Catalysis 219
  • Information Systems 483
  • Automotive Engineering 229
  • Renewable Energy, Sustainability and the Environment 302
Replace Jinlin Yang with:
Jinlin Yang China
Yajing Zhao China
Liqun Sun China
Chaonan Wang China
Rosana Balzer Brazil
Lili Bo China
Xudong Liu China
Xiaolei Ren China
Takeshi Ogasawara Japan
Jang‐Soo Lee South Korea
Xinli Yang relative to Jinlin Yang China Jinlin Yang's profile →
Citations per field
00.5×3.5×
Jinlin Yang · 1×
Citations per year

Countries citing papers authored by Xinli Yang

Since Specialization
Citations

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

Fields of papers citing papers by Xinli Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 80 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Learning for Just-in-Time Defect Prediction
Hit paper breakdown →
2015238
2 2021194
3 2017165
4 2018139
5 2022103
6 201391
7 201588
8 202284
9 200578
10 200873
11 201873
12 201671
13 201570
14 200569
15 201565
16 202364
17 202058
18 202142
19 202140
20 201739

About Xinli Yang

Xinli Yang is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Electronic, Optical and Magnetic Materials, Inorganic Chemistry and Organic Chemistry, having authored 80 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advancements in Battery Materials (19 papers), Advanced Battery Materials and Technologies (15 papers), Supercapacitor Materials and Fabrication (13 papers), Polyoxometalates: Synthesis and Applications (12 papers), Advanced battery technologies research (11 papers), Mesoporous Materials and Catalysis (8 papers), Software Engineering Research (6 papers) and Metal-Organic Frameworks: Synthesis and Applications (6 papers). The work is most often cited by research in Software (370 citations), Catalysis (219 citations), Information Systems (483 citations), Automotive Engineering (229 citations) and Renewable Energy, Sustainability and the Environment (302 citations). Xinli Yang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Xin Xia, David Lo, Xiaoyu Cao, Lingling Xie, Limin Zhu, Wei‐Lin Dai, Qing Han, Tao Zhou, Kangnian Fan and Wenlei Xie. Their work appears in journals such as Applied Catalysis A General, Information and Software Technology, CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION), Rare Metals and Ceramics International.

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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