Dawei Yin
- Information Systems top 5%
- Web Data Mining and Analysis 7
- Information Retrieval and Search Behavior 5
- Recommender Systems and Techniques 2
- Artificial Intelligence top 5%
- Topic Modeling 2
- Statistics and Probability top 10%
- Advanced Causal Inference Techniques 3
- Statistical Methods and Bayesian Inference 1
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- Consumer Market Behavior and Pricing 5
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- Data Management and Algorithms 2
- Co-authors
- Yi ChangJiliang TangPengyuan WangChangsung KangMakoto YamadaYuening HuHua OuyangSuhang Wang
- Journals
- Neurocomputing (1 paper)IEEE Transactions on Knowledge and Data Engineering (2 papers)Institutional Research Information System University of Turin (University of Turin) (1 paper)
- Partner nations
- United StatesChinaJapan
In The Last Decade
Dawei Yin
17 papers receiving 391 citations
Peers
Comparison fields: 5 of 59
- Information Systems 205
- Artificial Intelligence 233
- Computer Vision and Pattern Recognition 95
- Statistics and Probability 38
- Statistical and Nonlinear Physics 33
Countries citing papers authored by Dawei Yin
This map shows the geographic impact of Dawei Yin'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 Dawei Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dawei Yin more than expected).
Fields of papers citing papers by Dawei Yin
This network shows the impact of papers produced by Dawei Yin. 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 Dawei Yin. The network helps show where Dawei Yin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dawei Yin, 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 | 2019 | 11 | |
| 2 | 2018 | 54 | |
| 3 | 2017 | 5 | |
| 4 | 2016 | 79 | |
| 5 | Timeline summarization from social media with life cycle models | 2016 | 9 |
| 6 | 2016 | 77 | |
| 7 | 2016 | 19 | |
| 8 | 2016 | 17 | |
| 9 | 2016 | 34 | |
| 10 | 2016 | 39 | |
| 11 | 2015 | 11 | |
| 12 | 2015 | 20 | |
| 13 | 2015 | 1 | |
| 14 | 2015 | 2 | |
| 15 | 2015 | 26 | |
| 16 | Triple helix interface organization and knowledge transfer in the innovation | 2008 | 1 |
| 17 | 2008 | 5 |
About Dawei Yin
Dawei Yin is a scholar working on Marketing, Information Systems and Statistics and Probability, having authored 17 papers that have together received 410 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (7 papers), Consumer Market Behavior and Pricing (5 papers), Information Retrieval and Search Behavior (5 papers), Advanced Causal Inference Techniques (3 papers), Topic Modeling (2 papers), Recommender Systems and Techniques (2 papers), Data Management and Algorithms (2 papers) and Statistical Methods and Bayesian Inference (1 paper). The work is most often cited by research in Information Systems (205 citations), Artificial Intelligence (233 citations) and Computer Vision and Pattern Recognition (95 citations). Dawei Yin has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Yi Chang, Jiliang Tang, Pengyuan Wang, Changsung Kang, Makoto Yamada, Yuening Hu, Hua Ouyang, Suhang Wang, Huan Liu and Xia Hu. Their work appears in journals such as Neurocomputing, IEEE Transactions on Knowledge and Data Engineering and Institutional Research Information System University of Turin (University of Turin).
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.