Mingxi Cheng

854 citations
25 papers · 583 · h-index 10

Impact in

Papers in

Mingxi Cheng

25 papers receiving 568 citations

Peers

Mingxi Cheng
Comparison fields: 5 of 93
  • Information Systems 255
  • Computer Networks and Communications 222
  • Health Informatics 8
  • Artificial Intelligence 161
  • Statistical and Nonlinear Physics 51
Replace Han Hee Song with:
Han Hee Song United States
Jing Li Zhou China
Wenjie Wang China
Cheng Long Xu China
Zhenyu Wang China
Jae C. Oh United States
Yu Wen China
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Citations per field
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Han Hee Song · 1×
Citations per year

Countries citing papers authored by Mingxi Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Mingxi Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018157
2 202374
3 202067
4 201861
5 202153
6 202032
7 202328
8 202122
9 202121
10 201921
11 20219
12 20246
13 20216
14 20245
15 20224
16 20153
17 20233
18 20183
19 20242
20
LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks
20191

About Mingxi Cheng

Mingxi Cheng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Information Systems and Sociology and Political Science, having authored 25 papers that have together received 583 indexed citations. Recurring topics across this work include Misinformation and Its Impacts (5 papers), Logic, Reasoning, and Knowledge (3 papers), Cloud Computing and Resource Management (3 papers), Advanced Neural Network Applications (3 papers), IoT and Edge/Fog Computing (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Topic Modeling (2 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (2 papers). The work is most often cited by research in Information Systems (255 citations), Computer Networks and Communications (222 citations), Health Informatics (8 citations), Artificial Intelligence (161 citations) and Statistical and Nonlinear Physics (51 citations). Mingxi Cheng has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Shahin Nazarian, Ji Li, Paul Bogdan, Xiongye Xiao, Songli Wang, Tianqi Yang, Yizhi Li, Xiaofeng Yan, Wenshuo Wang and Lucreția Udrescu. Their work appears in journals such as Scientific Reports, Advanced Science, Autonomous Agents and Multi-Agent Systems, Materials Letters and Proceedings of the National Academy of Sciences.

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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