Honghua Dai
- Information Systems top 0.5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 2%
- Computer Networks and Communications top 5%
- Co-authors
- Tao LuoBamshad MobasherMiki NakagawaDouglas R. HeisterkampJing PengGang LiXin GengYu Zhang
- Topics
- Data Mining Algorithms and Applications (12 papers)Bayesian Modeling and Causal Inference (8 papers)Machine Learning and Data Classification (8 papers)
- Partner nations
- AustraliaChinaUnited States
In The Last Decade
Honghua Dai
61 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 134
- Information Systems 844
- Artificial Intelligence 659
- Computer Vision and Pattern Recognition 508
- Signal Processing 282
- Computer Networks and Communications 227
Countries citing papers authored by Honghua Dai
This map shows the geographic impact of Honghua Dai'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 Honghua Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Honghua Dai more than expected).
Fields of papers citing papers by Honghua Dai
This network shows the impact of papers produced by Honghua Dai. 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 Honghua Dai. The network helps show where Honghua Dai may publish in the future.
Co-authorship network of co-authors of Honghua Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Honghua Dai. A scholar is included among the top collaborators of Honghua Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Honghua Dai. Honghua Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 14 | |
| 4 | 13 | |
| 5 | 7 | |
| 6 | 5 | |
| 7 | An efficient one-pass method for discovering bases of recently frequent episodes over online data streams | 2 |
| 8 | 4 | |
| 9 | 191 | |
| 10 | 2 | |
| 11 | 78 | |
| 12 | 6 | |
| 13 | 2 | |
| 14 | 2 | |
| 15 | 87 | |
| 16 | Forecasting from Low Quality Data with Applications in Weather Forecasting. | 8 |
| 17 | A study of causal discovery with weak links and small samples | 21 |
| 18 | 1 | |
| 19 | Causal Discovery via MML. | 44 |
| 20 | 60 |
About Honghua Dai
Honghua Dai is a scholar working on Artificial Intelligence, Information Systems and Health Informatics, having authored 63 papers that have together received 1.9k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (12 papers), Bayesian Modeling and Causal Inference (8 papers) and Machine Learning and Data Classification (8 papers). The work is most often cited by research in Information Systems (844 citations), Signal Processing (282 citations) and Computer Vision and Pattern Recognition (508 citations). Honghua Dai has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Tao Luo, Bamshad Mobasher, Miki Nakagawa, Douglas R. Heisterkamp, Jing Peng, Gang Li, Xin Geng, Yu Zhang, Zhi‐Hua Zhou and Ling Zhuang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Optics Letters and IEEE Access.
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.