Ying Fan
- Information Systems top 0.2%
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 1%
- Management Science and Operations Research top 2%
- Computer Networks and Communications top 5%
- Co-authors
- Guorui ZhouXiaoqiang ZhuKun GaiJunqi JinYanghui YanZhu HanXiao MaHan Li
- Topics
- Cloud Computing and Resource Management (4 papers)Advanced Neural Network Applications (3 papers)Machine Learning and Data Classification (3 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Ying Fan
23 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Information Systems 1.6k
- Artificial Intelligence 1.1k
- Computer Vision and Pattern Recognition 747
- Management Science and Operations Research 374
- Computer Networks and Communications 303
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 | 1 | |
| 2 | 1 | |
| 3 | 16 | |
| 4 | 130 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | Deep Interest Evolution Network for Click-Through Rate Predictionbreakdown → | 578 |
| 8 | 2 | |
| 9 | 74 | |
| 10 | 1 | |
| 11 | 33 | |
| 12 | Rocket Launching: A unified and effecient framework for training well-behaved light net | 2 |
| 13 | Instance Specific Discriminative Modal Pursuit: A Serialized Approach. | 2 |
| 14 | 34 | |
| 15 | 9 | |
| 16 | 2 | |
| 17 | 0 | |
| 18 | 3 | |
| 19 | 12 | |
| 20 | 2 |
About Ying Fan
Ying Fan is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Instrumentation, having authored 24 papers that have together received 2.1k indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (4 papers), Advanced Neural Network Applications (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Information Systems (1.6k citations), Computer Vision and Pattern Recognition (747 citations) and Artificial Intelligence (1.1k citations). Ying Fan has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Guorui Zhou, Xiaoqiang Zhu, Kun Gai, Junqi Jin, Yanghui Yan, Zhu Han, Xiao Ma, Han Li, Weijie Bian and Na Mou. Their work appears in journals such as Chaos Solitons & Fractals, Current Protein and Peptide Science and Frontiers of Computer Science.
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.