Linyao Yang
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Advanced Graph Neural Networks
- Natural Language Processing Techniques
Papers in
-
- Topic Modeling 7
- Advanced Graph Neural Networks 4
- Natural Language Processing Techniques 2
-
- Data Quality and Management 4
- Co-authors
- Xiao Wang (10 shared papers)Fei‐Yue Wang (12 shared papers)Shuangshuang Han (4 shared papers)Xiao Ding (1 shared paper)Hongyang Chen (2 shared papers)Xindong Wu (1 shared paper)Zhao Li (1 shared paper)Tingting Yao (1 shared paper)
- Journals
- IEEE Transactions on Computational Social Systems (3 papers)IEEE Internet of Things Journal (2 papers)Information Fusion (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Knowledge-Based Systems (1 paper)
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
Linyao Yang
12 papers receiving 241 citations
Linyao Yang's Hit Papers
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 99
- Health Informatics 4
- Ocean Engineering 33
- Automotive Engineering 24
- Computer Networks and Communications 45
Countries citing papers authored by Linyao Yang
This map shows the geographic impact of Linyao 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 Linyao Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linyao Yang more than expected).
Fields of papers citing papers by Linyao Yang
This network shows the impact of papers produced by Linyao 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 Linyao Yang. The network helps show where Linyao Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Linyao 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 | Give us the Facts: Enhancing Large Language Models With Knowledge Graphs for Fact-Aware Language Modeling Hit paper breakdown → | 2024 | 69 |
| 2 | 2020 | 38 | |
| 3 | 2021 | 30 | |
| 4 | 2018 | 25 | |
| 5 | 2020 | 22 | |
| 6 | 2022 | 17 | |
| 7 | 2018 | 17 | |
| 8 | 2022 | 13 | |
| 9 | 2021 | 11 | |
| 10 | 2021 | 7 | |
| 11 | 2024 | 2 | |
| 12 | 2020 | 1 | |
| 13 | 2025 | 0 | |
| 14 | 2025 | 0 | |
| 15 | 2024 | 0 |
About Linyao Yang
Linyao Yang is a scholar working on Artificial Intelligence, Management Science and Operations Research, Electrical and Electronic Engineering, Ocean Engineering and Safety, Risk, Reliability and Quality, having authored 15 papers that have together received 252 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Advanced Graph Neural Networks (4 papers), Data Quality and Management (4 papers), Vehicular Ad Hoc Networks (VANETs) (3 papers), Evacuation and Crowd Dynamics (3 papers), Natural Language Processing Techniques (2 papers), IoT Networks and Protocols (2 papers) and Wireless Body Area Networks (2 papers). The work is most often cited by research in Artificial Intelligence (99 citations), Health Informatics (4 citations), Ocean Engineering (33 citations), Automotive Engineering (24 citations) and Computer Networks and Communications (45 citations). Linyao Yang has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Xiao Wang, Fei‐Yue Wang, Shuangshuang Han, Xiao Ding, Hongyang Chen, Xindong Wu, Zhao Li, Tingting Yao, Lingxi Li and Yuke Li. Their work appears in journals such as IEEE Transactions on Computational Social Systems, IEEE Internet of Things Journal, Information Fusion, IEEE Transactions on Knowledge and Data Engineering and Knowledge-Based 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.