Ran Yu
Impact in
- Computer Science Applications top 10%
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Semantic Web and Ontologies
Papers in
-
- Topic Modeling 6
- Intelligent Tutoring Systems and Adaptive Learning 4
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- Information Retrieval and Search Behavior 9
- Expert finding and Q&A systems 4
- Web Data Mining and Analysis 4
- Co-authors
- Stefan Dietze (10 shared papers)Ujwal Gadiraju (5 shared papers)Peter Holtz (2 shared papers)Jiqun Liu (6 shared papers)Xin Cui (1 shared paper)Haozhe Ji (1 shared paper)Minlie Huang (1 shared paper)Liwei Wang (1 shared paper)
- Journals
- Information Retrieval (2 papers)Semantic Web (1 paper)Neurocomputing (1 paper)Spine (1 paper)ACM SIGIR Forum (1 paper)
- Partner nations
- GermanyChinaUnited States
In The Last Decade
Ran Yu
25 papers receiving 204 citations
Peers
Comparison fields: 5 of 50
- Computer Science Applications 28
- Artificial Intelligence 115
- Information Systems 67
- Communication 15
- Developmental and Educational Psychology 22
Countries citing papers authored by Ran Yu
This map shows the geographic impact of Ran Yu'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 Ran Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ran Yu more than expected).
Fields of papers citing papers by Ran Yu
This network shows the impact of papers produced by Ran Yu. 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 Ran Yu. The network helps show where Ran Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Ran Yu, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 53 | |
| 2 | 2018 | 52 | |
| 3 | 2018 | 12 | |
| 4 | 2022 | 11 | |
| 5 | 2011 | 10 | |
| 6 | 2021 | 10 | |
| 7 | 2018 | 9 | |
| 8 | 2021 | 7 | |
| 9 | 2017 | 7 | |
| 10 | 2011 | 5 | |
| 11 | 2021 | 5 | |
| 12 | 2017 | 4 | |
| 13 | 2023 | 3 | |
| 14 | 2022 | 3 | |
| 15 | 2025 | 3 | |
| 16 | A Survey on Challenges in Web Markup Data for Entity Retrieval. | 2016 | 3 |
| 17 | 2020 | 2 | |
| 18 | 2019 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2024 | 1 |
About Ran Yu
Ran Yu is a scholar working on Artificial Intelligence, Information Systems, Computer Science Applications, Electrical and Electronic Engineering and Communication, having authored 30 papers that have together received 208 indexed citations. Recurring topics across this work include Information Retrieval and Search Behavior (9 papers), Topic Modeling (6 papers), Wikis in Education and Collaboration (4 papers), Expert finding and Q&A systems (4 papers), Web Data Mining and Analysis (4 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers), Open Education and E-Learning (3 papers) and Power Systems Fault Detection (3 papers). The work is most often cited by research in Computer Science Applications (28 citations), Artificial Intelligence (115 citations), Information Systems (67 citations), Communication (15 citations) and Developmental and Educational Psychology (22 citations). Ran Yu has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Stefan Dietze, Ujwal Gadiraju, Peter Holtz, Jiqun Liu, Xin Cui, Haozhe Ji, Minlie Huang, Liwei Wang, Pei Ke and Xiaoyan Zhu. Their work appears in journals such as Information Retrieval, Semantic Web, Neurocomputing, Spine and ACM SIGIR Forum.
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.