Dayong Ye

2.1k citations
75 papers · 1.3k · h-index 20

Impact in

Papers in

Dayong Ye

71 papers receiving 1.3k citations

Peers

Dayong Ye
Comparison fields: 5 of 89
  • Computer Networks and Communications 564
  • Artificial Intelligence 552
  • Information Systems 352
  • Computer Science Applications 75
  • Signal Processing 85
Replace Nawab Muhammad Faseeh Qureshi with:
Nawab Muhammad Faseeh Qureshi South Korea
Minrui Xu China
Tianyi Chen United States
Wajid Rafique China
Mohd Zakree Ahmad Nazri Malaysia
Xiaoxian Yang China
Tao Yue Norway
J. Morris Chang United States
Costin Bădică Romania
Fuyuki Ishikawa Japan
Dayong Ye relative to Nawab Muhammad Faseeh Qureshi South Korea Nawab Muhammad Faseeh Qureshi's profile →
Citations per field
00.5×10×17×
Nawab Muhammad Faseeh Qureshi · 1×
Citations per year

Countries citing papers authored by Dayong Ye

Since Specialization
Citations

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

Fields of papers citing papers by Dayong Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016163
2 2017100
3 201696
4 202272
5 201172
6 202163
7 201556
8 202253
9 201646
10 201344
11 201736
12 202033
13 202233
14 201929
15 202228
16 201723
17 202223
18 202122
19 201621
20 202020

About Dayong Ye

Dayong Ye is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Sociology and Political Science and Computer Science Applications, having authored 75 papers that have together received 1.3k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (23 papers), Adversarial Robustness in Machine Learning (12 papers), Service-Oriented Architecture and Web Services (10 papers), Network Security and Intrusion Detection (9 papers), Mobile Crowdsensing and Crowdsourcing (9 papers), Peer-to-Peer Network Technologies (8 papers), Advanced Software Engineering Methodologies (7 papers) and Evolutionary Game Theory and Cooperation (6 papers). The work is most often cited by research in Computer Networks and Communications (564 citations), Artificial Intelligence (552 citations), Information Systems (352 citations), Computer Science Applications (75 citations) and Signal Processing (85 citations). Dayong Ye has collaborated with scholars based in Australia, Macao and China. Frequent co-authors include Minjie Zhang, Wanlei Zhou, Tianqing Zhu, Yan Kong, Danny Sutanto, Philip S. Yu, Athanasios V. Vasilakos, Yanchun Wang, Yun Yang and Qiang He. Their work appears in journals such as Knowledge-Based Systems, IEEE Transactions on Information Forensics and Security, IEEE Internet of Things Journal, IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Cybernetics.

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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