Yuening Hu
- Artificial Intelligence top 5%
- General Social Sciences top 0.2%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Sociology and Political Science
- Co-authors
- Jordan Boyd‐GraberAlison SmithDavid MimnoDawei YinChangsung KangYi ChangKe ZhaiVladimir Eidelman
- Topics
- Computational and Text Analysis Methods (7 papers)Topic Modeling (6 papers)Web Data Mining and Analysis (4 papers)
- Journals
- Proceedings of the National Academy of SciencesIEEE Transactions on Knowledge and Data EngineeringMachine Learning
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Yuening Hu
20 papers receiving 610 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 429
- General Social Sciences 143
- Information Systems 138
- Computer Vision and Pattern Recognition 100
- Sociology and Political Science 69
Countries citing papers authored by Yuening Hu
This map shows the geographic impact of Yuening Hu'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 Yuening Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuening Hu more than expected).
Fields of papers citing papers by Yuening Hu
This network shows the impact of papers produced by Yuening Hu. 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 Yuening Hu. The network helps show where Yuening Hu may publish in the future.
Co-authorship network of co-authors of Yuening Hu
This figure shows the co-authorship network connecting the top 25 collaborators of Yuening Hu. A scholar is included among the top collaborators of Yuening Hu 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 Yuening Hu. Yuening Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 36 | |
| 4 | Applications of Topic Modelsbreakdown → | 128 |
| 5 | 31 | |
| 6 | 5 | |
| 7 | 77 | |
| 8 | 34 | |
| 9 | 2 | |
| 10 | 15 | |
| 11 | 19 | |
| 12 | 7 | |
| 13 | Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations | 5 |
| 14 | Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent | 4 |
| 15 | Interactive topic modelingbreakdown → | 232 |
| 16 | Efficient Tree-Based Topic Modeling | 8 |
| 17 | 7 | |
| 18 | 10 | |
| 19 | 0 | |
| 20 | 3 |
About Yuening Hu
Yuening Hu is a scholar working on General Social Sciences, Artificial Intelligence and Signal Processing, having authored 22 papers that have together received 663 indexed citations. Recurring topics across this work include Computational and Text Analysis Methods (7 papers), Topic Modeling (6 papers) and Web Data Mining and Analysis (4 papers). The work is most often cited by research in General Social Sciences (143 citations), Artificial Intelligence (429 citations) and Information Systems (138 citations). Yuening Hu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jordan Boyd‐Graber, Alison Smith, David Mimno, Dawei Yin, Changsung Kang, Yi Chang, Ke Zhai, Vladimir Eidelman, Jiliang Tang and Aaron Gerow. Their work appears in journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Knowledge and Data Engineering and Machine Learning.
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.