Ying Fan
- Statistical and Nonlinear Physics top 0.5%
- Molecular Biology
- Genetics top 5%
- Artificial Intelligence top 5%
- Statistics, Probability and Uncertainty top 0.5%
- Topics
- Complex Network Analysis Techniques (86 papers)Opinion Dynamics and Social Influence (54 papers)scientometrics and bibliometrics research (15 papers)
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Ying Fan
115 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 189
- Statistical and Nonlinear Physics 1.1k
- Molecular Biology 593
- Genetics 376
- Artificial Intelligence 358
- Statistics, Probability and Uncertainty 330
Countries citing papers authored by Ying Fan
This map shows the geographic impact of Ying 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 Ying Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Fan more than expected).
Fields of papers citing papers by Ying Fan
This network shows the impact of papers produced by Ying 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 Ying Fan. The network helps show where Ying Fan may publish in the future.
Co-authorship network of co-authors of Ying Fan
This figure shows the co-authorship network connecting the top 25 collaborators of Ying Fan. A scholar is included among the top collaborators of Ying 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 Ying Fan. Ying 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 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 5 | |
| 6 | 5 | |
| 7 | 6 | |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 24 | |
| 11 | 9 | |
| 12 | 23 | |
| 13 | 111 | |
| 14 | A CLUSTERING ALGORITHM FOR BIPARTITE NETWORK BASED ON DISTRIBUTION MATRIX OF RESOURCES | 3 |
| 15 | Study on Bipartite Networks | 3 |
| 16 | EMPIRICAL RESEARCH OF MAZE BASED ON THEORY OF COMPLEX NETWORK | 1 |
| 17 | 7 | |
| 18 | Comparative Definition of Community in Bipartite Network | 3 |
| 19 | Community Structure in Complex Networks | 4 |
| 20 | Restricted Edge-Connectivity of a Kind of Kautz Graphs | 1 |
About Ying Fan
Ying Fan is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and General Engineering, having authored 124 papers that have together received 2.7k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (86 papers), Opinion Dynamics and Social Influence (54 papers) and scientometrics and bibliometrics research (15 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.1k citations), Statistics, Probability and Uncertainty (330 citations) and Genetics (376 citations). Ying Fan has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Zengru Di, An Zeng, Menghui Li, Jinshan Wu, Zhesi Shen, Jianlin Zhou, Chengfeng Zhao, James L. Weber, Liang Gao and Yougui Wang. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.
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.