Junting Ye
Impact in
- Information Systems top 5%
- Spam and Phishing Detection
- Artificial Intelligence top 10%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
Papers in
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- Topic Modeling 3
- Sentiment Analysis and Opinion Mining 2
- Bayesian Modeling and Causal Inference 2
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- Spam and Phishing Detection 4
- Web Data Mining and Analysis 2
- Co-authors
- Leman Akoglu (4 shared papers)Steven Skiena (2 shared papers)Steve Skiena (1 shared paper)Qinghua Zheng (4 shared papers)William Yang Wang (1 shared paper)Vivek Kulkarni (1 shared paper)Tetsuya Sakai (2 shared papers)Jun Liu (1 shared paper)
- Journals
- Patient Preference and Adherence (1 paper)Information Retrieval (1 paper)Lecture notes in computer science (3 papers)Journal of Networks (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)
- Partner nations
- United StatesChinaJapan
In The Last Decade
Junting Ye
12 papers receiving 260 citations
Peers
Comparison fields: 5 of 40
- Information Systems 157
- Artificial Intelligence 174
- Signal Processing 42
- General Social Sciences 10
- Management Science and Operations Research 36
Countries citing papers authored by Junting Ye
This map shows the geographic impact of Junting Ye'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 Junting Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junting Ye more than expected).
Fields of papers citing papers by Junting Ye
This network shows the impact of papers produced by Junting Ye. 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 Junting Ye. The network helps show where Junting Ye may publish in the future.
Co-authors
The 16 scholars most cited alongside Junting Ye, 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 | 68 | |
| 2 | 2015 | 63 | |
| 3 | 2018 | 45 | |
| 4 | 2015 | 35 | |
| 5 | 2013 | 15 | |
| 6 | 2019 | 14 | |
| 7 | 2019 | 12 | |
| 8 | 2021 | 12 | |
| 9 | 2018 | 6 | |
| 10 | 2018 | 3 | |
| 11 | 2012 | 2 | |
| 12 | 2014 | 1 |
About Junting Ye
Junting Ye is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Computer Networks and Communications and Management Science and Operations Research, having authored 12 papers that have together received 276 indexed citations. Recurring topics across this work include Spam and Phishing Detection (4 papers), Topic Modeling (3 papers), Data Quality and Management (3 papers), Network Security and Intrusion Detection (3 papers), Web Data Mining and Analysis (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Media Influence and Politics (2 papers). The work is most often cited by research in Information Systems (157 citations), Artificial Intelligence (174 citations), Signal Processing (42 citations), General Social Sciences (10 citations) and Management Science and Operations Research (36 citations). Junting Ye has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Leman Akoglu, Steven Skiena, Steve Skiena, Qinghua Zheng, William Yang Wang, Vivek Kulkarni, Tetsuya Sakai, Jun Liu, Cong Li and Mingkang Zhong. Their work appears in journals such as Patient Preference and Adherence, Information Retrieval, Lecture notes in computer science, Journal of Networks and Proceedings of the International AAAI Conference on Web and Social Media.
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.