Chun-Ta Lu
Impact in
- Computational Mathematics top 5%
-
- FinTech, Crowdfunding, Digital Finance
Papers in
-
- Advanced Graph Neural Networks 12
- Topic Modeling 5
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- Recommender Systems and Techniques 9
- Spam and Phishing Detection 4
- Co-authors
- Philip S. Yu (24 shared papers)Lifang He (12 shared papers)Sihong Xie (4 shared papers)Weixiang Shao (5 shared papers)Xiangnan Kong (1 shared paper)Ann Ragin (5 shared papers)Bokai Cao (4 shared papers)Guixiang Ma (3 shared papers)
- Journals
- Knowledge and Information Systems (1 paper)International Joint Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesChinaSwitzerland
In The Last Decade
Chun-Ta Lu
31 papers receiving 638 citations
Peers
Comparison fields: 5 of 69
- Computational Mathematics 42
- Management Information Systems 128
- Statistical and Nonlinear Physics 120
- Artificial Intelligence 309
- Computer Vision and Pattern Recognition 162
Countries citing papers authored by Chun-Ta Lu
This map shows the geographic impact of Chun-Ta Lu'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 Chun-Ta Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chun-Ta Lu more than expected).
Fields of papers citing papers by Chun-Ta Lu
This network shows the impact of papers produced by Chun-Ta Lu. 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 Chun-Ta Lu. The network helps show where Chun-Ta Lu may publish in the future.
Co-authors
The 25 scholars most cited alongside Chun-Ta Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 145 | |
| 2 | 2016 | 82 | |
| 3 | 2016 | 51 | |
| 4 | 2017 | 43 | |
| 5 | 2017 | 36 | |
| 6 | 2017 | 35 | |
| 7 | 2017 | 26 | |
| 8 | 2017 | 23 | |
| 9 | 2014 | 21 | |
| 10 | 2019 | 17 | |
| 11 | 2017 | 16 | |
| 12 | 2019 | 15 | |
| 13 | 2020 | 14 | |
| 14 | 2017 | 13 | |
| 15 | Item recommendation for emerging online businesses | 2016 | 12 |
| 16 | 2016 | 12 | |
| 17 | 2020 | 12 | |
| 18 | 2017 | 11 | |
| 19 | 2019 | 10 | |
| 20 | 2016 | 8 |
About Chun-Ta Lu
Chun-Ta Lu is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computational Mathematics and Statistical and Nonlinear Physics, having authored 31 papers that have together received 651 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (12 papers), Recommender Systems and Techniques (9 papers), Tensor decomposition and applications (6 papers), Multimodal Machine Learning Applications (5 papers), Complex Network Analysis Techniques (5 papers), Topic Modeling (5 papers), Spam and Phishing Detection (4 papers) and Functional Brain Connectivity Studies (4 papers). The work is most often cited by research in Computational Mathematics (42 citations), Management Information Systems (128 citations), Statistical and Nonlinear Physics (120 citations), Artificial Intelligence (309 citations) and Computer Vision and Pattern Recognition (162 citations). Chun-Ta Lu has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Philip S. Yu, Lifang He, Sihong Xie, Weixiang Shao, Xiangnan Kong, Ann Ragin, Bokai Cao, Guixiang Ma, Linlin Shen and Lei Zheng. Their work appears in journals such as Knowledge and Information Systems, International Joint Conference on Artificial Intelligence and arXiv (Cornell University).
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.