Dingyi Han
- Artificial Intelligence top 5%
- Advanced Text Analysis Techniques 3
- Information Systems top 5%
- Expert finding and Q&A systems 4
- Web Data Mining and Analysis 4
- Recommender Systems and Techniques 3
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- Complex Network Analysis Techniques 6
- Opinion Dynamics and Social Influence 3
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- Peer-to-Peer Network Technologies 8
- Caching and Content Delivery 5
- Co-authors
- Yong YuShenghua BaoZhong SuShengliang XuRong YanHaibo HuLi ZhangGui-Rong Xue
- Journals
- Physica A Statistical Mechanics and its Applications (2 papers)Computer Communications (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)
- Partner nations
- ChinaUnited StatesIsrael
In The Last Decade
Dingyi Han
18 papers receiving 388 citations
Peers
Comparison fields: 5 of 55
- Computer Science Applications 63
- Artificial Intelligence 297
- Information Systems 198
- Statistical and Nonlinear Physics 74
- Communication 21
Countries citing papers authored by Dingyi Han
This map shows the geographic impact of Dingyi Han'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 Dingyi Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dingyi Han more than expected).
Fields of papers citing papers by Dingyi Han
This network shows the impact of papers produced by Dingyi Han. 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 Dingyi Han. The network helps show where Dingyi Han may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dingyi Han, 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 | 2015 | 15 | |
| 2 | Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising | 2014 | 41 |
| 3 | 2011 | 12 | |
| 4 | 2011 | 4 | |
| 5 | 2011 | 54 | |
| 6 | 2011 | 108 | |
| 7 | 2010 | 2 | |
| 8 | 2009 | 1 | |
| 9 | 2009 | 47 | |
| 10 | 2009 | 13 | |
| 11 | 2008 | 31 | |
| 12 | 2008 | 71 | |
| 13 | 2008 | 1 | |
| 14 | 2007 | 2 | |
| 15 | 2007 | 2 | |
| 16 | 2006 | 3 | |
| 17 | 2006 | 1 | |
| 18 | 2006 | 1 | |
| 19 | 2005 | 0 |
About Dingyi Han
Dingyi Han is a scholar working on Statistical and Nonlinear Physics, Information Systems, Computer Networks and Communications, Artificial Intelligence and Computer Science Applications, having authored 19 papers that have together received 409 indexed citations. Recurring topics across this work include Peer-to-Peer Network Technologies (8 papers), Complex Network Analysis Techniques (6 papers), Caching and Content Delivery (5 papers), Expert finding and Q&A systems (4 papers), Web Data Mining and Analysis (4 papers), Recommender Systems and Techniques (3 papers), Opinion Dynamics and Social Influence (3 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Computer Science Applications (63 citations), Artificial Intelligence (297 citations), Information Systems (198 citations), Statistical and Nonlinear Physics (74 citations) and Communication (21 citations). Dingyi Han has collaborated with scholars based in China, United States and Israel. Frequent co-authors include Yong Yu, Shenghua Bao, Zhong Su, Shengliang Xu, Rong Yan, Haibo Hu, Li Zhang, Gui-Rong Xue, Yuanjie Liu and Chin-Yew Lin. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Computer Communications, IEEE Transactions on Knowledge and Data Engineering, Proceedings - IEEE International Parallel and Distributed Processing Symposium and International Conference on Machine Learning.
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.