Liyue Fan
- Artificial Intelligence top 2%
- Computer Science Applications top 1%
- Sociology and Political Science top 10%
- Electrical and Electronic Engineering
- Transportation top 5%
- Topics
- Privacy-Preserving Technologies in Data (27 papers)Mobile Crowdsensing and Crowdsourcing (10 papers)Privacy, Security, and Data Protection (8 papers)
- Journals
- IEEE Transactions on Knowledge and Data EngineeringJournal of the American Medical Informatics AssociationIEEE Transactions on Mobile Computing
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Liyue Fan
36 papers receiving 777 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 576
- Computer Science Applications 305
- Sociology and Political Science 140
- Electrical and Electronic Engineering 135
- Transportation 134
Countries citing papers authored by Liyue Fan
This map shows the geographic impact of Liyue Fan'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 Liyue Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liyue Fan more than expected).
Fields of papers citing papers by Liyue Fan
This network shows the impact of papers produced by Liyue Fan. 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 Liyue Fan. The network helps show where Liyue Fan may publish in the future.
Co-authorship network of co-authors of Liyue Fan
This figure shows the co-authorship network connecting the top 25 collaborators of Liyue Fan. A scholar is included among the top collaborators of Liyue Fan 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 Liyue Fan. Liyue Fan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 7 | |
| 9 | 8 | |
| 10 | 82 | |
| 11 | 10 | |
| 12 | 9 | |
| 13 | 18 | |
| 14 | 11 | |
| 15 | 7 | |
| 16 | 110 | |
| 17 | 9 | |
| 18 | Utilizing Real-World Transportation Data for Accurate Traffic Prediction | 1 |
| 19 | 31 | |
| 20 | 58 |
About Liyue Fan
Liyue Fan is a scholar working on Computer Science Applications, Artificial Intelligence and Transportation, having authored 39 papers that have together received 794 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (27 papers), Mobile Crowdsensing and Crowdsourcing (10 papers) and Privacy, Security, and Data Protection (8 papers). The work is most often cited by research in Computer Science Applications (305 citations), Transportation (134 citations) and Artificial Intelligence (576 citations). Liyue Fan has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Li Xiong, Cyrus Shahabi, Luan Tran, Hien To, Gabriel Ghinita, Luca Bonomi, Vaidy Sunderam, Hongxia Jin, Slawomir Goryczka and Minh Nguyen. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Journal of the American Medical Informatics Association and IEEE Transactions on Mobile Computing.
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.