Yunlong Mao

620 total citations
28 papers, 241 citations indexed

About

Yunlong Mao is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Yunlong Mao has authored 28 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 6 papers in Computer Networks and Communications and 6 papers in Information Systems. Recurrent topics in Yunlong Mao's work include Privacy-Preserving Technologies in Data (13 papers), Cryptography and Data Security (10 papers) and Adversarial Robustness in Machine Learning (5 papers). Yunlong Mao is often cited by papers focused on Privacy-Preserving Technologies in Data (13 papers), Cryptography and Data Security (10 papers) and Adversarial Robustness in Machine Learning (5 papers). Yunlong Mao collaborates with scholars based in China, United States and Canada. Yunlong Mao's co-authors include Sheng Zhong, Honghui Dong, Qing Zhang, Junwei Gao, Fengyuan Xu, Yuan Zhang, Qun Li, Yuan Zhang, Shanhe Yi and Heng Wang and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, Sensors and Neurocomputing.

In The Last Decade

Yunlong Mao

27 papers receiving 236 citations

Peers

Yunlong Mao
Comparison fields: 5 of 49
  • Artificial Intelligence 148
  • Computer Networks and Communications 67
  • Electrical and Electronic Engineering 52
  • Information Systems 44
  • Control and Systems Engineering 37
Replace István Hegedűs with:
István Hegedűs Hungary
Xiaofeng Yu Hong Kong
Kwihoon Kim South Korea
Xueqiang Wang China
Dane Brown United States
Yuchen Xie China
Bassem Ouni United Arab Emirates
Jiaming Li China
István Hegedűs Hungary View profile →
Citations per field, relative to Yunlong Mao
Yunlong Mao · 1×
Citations per year, relative to Yunlong Mao
Yunlong Mao · 1×

Countries citing papers authored by Yunlong Mao

Since Specialization
Citations

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

Fields of papers citing papers by Yunlong Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yunlong Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Yunlong Mao. A scholar is included among the top collaborators of Yunlong Mao 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 Yunlong Mao. Yunlong Mao 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
# Work Indexed citations
1 4
2 6
3 1
4 2
5 3
6 7
7 0
8 2
9 1
10 7
11 10
12 20
13 20
14 22
15 30
16 11
17 11
18 7
19 4
20 3

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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