Chaoyue Niu

700 citations
26 papers · 440 indexed · h-index 12
Topics
Privacy-Preserving Technologies in Data (21 papers)Cryptography and Data Security (9 papers)Mobile Crowdsensing and Crowdsourcing (5 papers)
Partner nations
ChinaUnited States

In The Last Decade

Chaoyue Niu

24 papers receiving 429 citations

Peers

Chaoyue Niu
Comparison fields: 5 of 45
  • Artificial Intelligence 336
  • Information Systems 132
  • Sociology and Political Science 86
  • Computer Networks and Communications 81
  • Computer Science Applications 73
Replace Slava Novgorodov with:
Slava Novgorodov Israel
Prasang Upadhyaya United States
Jordi Soria-Comas Spain
Harald Psaier Austria
Jordi Castellà‐Roca Spain
Xitong Li China
Iman Saleh United States
Keivan Kianmehr Canada
Akash Das Sarma United States
Sheetal Girase India
Chaoyue Niu relative to Slava Novgorodov Israel Slava Novgorodov's profile →
Citations per field
00.5×5.3×
Slava Novgorodov · 1×
Citations per year

Countries citing papers authored by Chaoyue Niu

Since Specialization
Citations

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

Fields of papers citing papers by Chaoyue Niu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chaoyue Niu

This figure shows the co-authorship network connecting the top 25 collaborators of Chaoyue Niu. A scholar is included among the top collaborators of Chaoyue Niu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chaoyue Niu. Chaoyue Niu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 2
3 30
4 1
5 5
6 1
7 1
8 14
9 11
10 19
11 32
12 88
13 11
14 26
15 32
16 2
17 30
18 7
19 2
20 1

About Chaoyue Niu

Chaoyue Niu is a scholar working on Computer Science Applications, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 26 papers that have together received 440 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (21 papers), Cryptography and Data Security (9 papers) and Mobile Crowdsensing and Crowdsourcing (5 papers). The work is most often cited by research in Computer Science Applications (73 citations), Artificial Intelligence (336 citations) and Information Systems (132 citations). Chaoyue Niu has collaborated with scholars based in China and United States. Frequent co-authors include Fan Wu, Guihai Chen, Shaojie Tang, Zhenzhe Zheng, Xiaofeng Gao, Zhihua Wu, Rongfei Jia, Yihao Xue, Shuai Ma and Honglan Huang. Their work appears in journals such as ACM Computing Surveys, Information Sciences and IEEE Transactions on Neural Networks and Learning Systems.

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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