Wubai Zhou
- Artificial Intelligence top 5%
- Text and Document Classification Technologies 8
- Data Stream Mining Techniques 4
- Information Systems top 5%
- Web Data Mining and Analysis 10
- Data Mining Algorithms and Applications 5
- Service-Oriented Architecture and Web Services 3
- Spam and Phishing Detection 2
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- Software System Performance and Reliability 3
- Health Information Management top 10%
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- Geographic Information Systems Studies 3
- Co-authors
- Tao LiChunqiu ZengWei XueLarisa ShwartzGenady Ya. GrabarnikYexi JiangS. S. IyengarNing Xie
- Journals
- ACM Computing Surveys (2 papers)IEEE Transactions on Knowledge and Data Engineering (1 paper)Future Generation Computer Systems (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Wubai Zhou
24 papers receiving 646 citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 343
- Information Systems 227
- Management Science and Operations Research 93
- Computer Networks and Communications 157
- Health Information Management 22
Countries citing papers authored by Wubai Zhou
This map shows the geographic impact of Wubai Zhou'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 Wubai Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wubai Zhou more than expected).
Fields of papers citing papers by Wubai Zhou
This network shows the impact of papers produced by Wubai Zhou. 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 Wubai Zhou. The network helps show where Wubai Zhou may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Wubai Zhou, 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 | 2018 | 44 | |
| 2 | 2018 | 7 | |
| 3 | 2018 | 2 | |
| 4 | 2018 | 2 | |
| 5 | Data-Driven Techniques in Disaster Information Managementbreakdown → | 2017 | 250 |
| 6 | MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews | 2017 | 34 |
| 7 | 2017 | 28 | |
| 8 | 2017 | 3 | |
| 9 | 2017 | 21 | |
| 10 | 2017 | 17 | |
| 11 | 2017 | 11 | |
| 12 | 2016 | 21 | |
| 13 | 2016 | 58 | |
| 14 | 2016 | 7 | |
| 15 | 2015 | 2 | |
| 16 | 2015 | 25 | |
| 17 | 2015 | 12 | |
| 18 | 2014 | 17 | |
| 19 | 2014 | 18 | |
| 20 | 2013 | 27 |
About Wubai Zhou
Wubai Zhou is a scholar working on Information Systems, Geography, Planning and Development and Artificial Intelligence, having authored 24 papers that have together received 667 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (10 papers), Text and Document Classification Technologies (8 papers), Data Mining Algorithms and Applications (5 papers), Data Stream Mining Techniques (4 papers), Service-Oriented Architecture and Web Services (3 papers), Geographic Information Systems Studies (3 papers), Software System Performance and Reliability (3 papers) and Spam and Phishing Detection (2 papers). The work is most often cited by research in Artificial Intelligence (343 citations), Information Systems (227 citations) and Management Science and Operations Research (93 citations). Wubai Zhou has collaborated with scholars based in United States and China. Frequent co-authors include Tao Li, Chunqiu Zeng, Wei Xue, Larisa Shwartz, Genady Ya. Grabarnik, Yexi Jiang, S. S. Iyengar, Ning Xie, Shu‐Ching Chen and Zheng Li. Their work appears in journals such as ACM Computing Surveys, IEEE Transactions on Knowledge and Data Engineering and Future Generation Computer Systems.
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.