Peng Yang
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- IoT and Edge/Fog Computing 8
- Opportunistic and Delay-Tolerant Networks 6
- Information Systems top 2%
- Recommender Systems and Techniques 7
- Signal Processing top 5%
- Artificial Intelligence top 5%
- Topic Modeling 21
- Natural Language Processing Techniques 10
- Advanced Graph Neural Networks 8
- Advanced Text Analysis Techniques 6
- Privacy-Preserving Technologies in Data 6
- General Energy top 10%
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Peng Yang
77 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 115
- Computer Networks and Communications 361
- Information Systems 273
- Signal Processing 128
- Artificial Intelligence 311
- General Energy 9
Countries citing papers authored by Peng Yang
This map shows the geographic impact of Peng 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 Peng Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Yang more than expected).
Fields of papers citing papers by Peng Yang
This network shows the impact of papers produced by Peng 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 Peng Yang. The network helps show where Peng Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peng Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 2 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 20 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 13 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 0 | |
| 12 | 2022 | 29 | |
| 13 | 2022 | 4 | |
| 14 | 2017 | 1 | |
| 15 | On the decision-making problem of stockpiling position of containers in storage yard based on GA | 2011 | 1 |
| 16 | On robust discrete berth allocation based on ant colony algorithm | 2010 | 3 |
| 17 | On the optimization of yard crane scheduling in a container terminal based on GATS | 2010 | 1 |
| 18 | 2010 | 7 | |
| 19 | Studies on separation and purification of alkaloid from lotus leaves and inhibition effects of extracts on lipase activity. | 2009 | 1 |
| 20 | 2009 | 3 |
About Peng Yang
Peng Yang is a scholar working on Artificial Intelligence, General Energy and Computer Networks and Communications, having authored 91 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (10 papers), IoT and Edge/Fog Computing (8 papers), Advanced Graph Neural Networks (8 papers), Recommender Systems and Techniques (7 papers), Advanced Text Analysis Techniques (6 papers), Opportunistic and Delay-Tolerant Networks (6 papers) and Privacy-Preserving Technologies in Data (6 papers). The work is most often cited by research in Computer Networks and Communications (361 citations), Information Systems (273 citations) and Signal Processing (128 citations). Peng Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Guangzhen Zhao, Peng Zeng, Xuan Liu, Zhuo Li, Biao Huang, Mingju Gong, Yin Bai, Sheng Wang, Jin Wang and Juan Qin. Their work appears in journals such as Biochemistry, Scientific Reports and Nanoscale.
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.