David N. Chin
- Artificial Intelligence top 2%
- Information Systems top 5%
- Human-Computer Interaction top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Robert WilenskyYigal ArensAlex QuiliciAlfred KobsaKaren Spärck JonesMarlene JonesRobin CohenA. Jameson
- Topics
- Multi-Agent Systems and Negotiation (13 papers)AI-based Problem Solving and Planning (9 papers)Natural Language Processing Techniques (7 papers)
- Partner nations
- United StatesTaiwanHong Kong
In The Last Decade
David N. Chin
38 papers receiving 634 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 482
- Information Systems 194
- Human-Computer Interaction 92
- Computer Networks and Communications 79
- Computer Vision and Pattern Recognition 77
Countries citing papers authored by David N. Chin
This map shows the geographic impact of David N. Chin'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 David N. Chin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David N. Chin more than expected).
Fields of papers citing papers by David N. Chin
This network shows the impact of papers produced by David N. Chin. 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 David N. Chin. The network helps show where David N. Chin may publish in the future.
Co-authorship network of co-authors of David N. Chin
This figure shows the co-authorship network connecting the top 25 collaborators of David N. Chin. A scholar is included among the top collaborators of David N. Chin 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 David N. Chin. David N. Chin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Social Media Sources for Personality Profiling. | 13 |
| 2 | 3 | |
| 3 | 1 | |
| 4 | Computable social communication | 0 |
| 5 | 7 | |
| 6 | 4 | |
| 7 | 1 | |
| 8 | Investigating User Comprehension of Complex Multi-user Interfaces | 2 |
| 9 | Evaluating Multi-User Interfaces (EMI). | 1 |
| 10 | 1 | |
| 11 | 17 | |
| 12 | Spatial-Linguistic Reasoning in LEI (Locality and Elevation Interpreter) | 5 |
| 13 | 2 | |
| 14 | Exploiting user expertise in answer expression | 11 |
| 15 | User models and discourse models | 2 |
| 16 | 1 | |
| 17 | 29 | |
| 18 | 11 | |
| 19 | A case study of knowledge representation in UC | 5 |
| 20 | 4 |
About David N. Chin
David N. Chin is a scholar working on Software, Artificial Intelligence and Information Systems, having authored 41 papers that have together received 756 indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (13 papers), AI-based Problem Solving and Planning (9 papers) and Natural Language Processing Techniques (7 papers). The work is most often cited by research in Human-Computer Interaction (92 citations), Artificial Intelligence (482 citations) and Computer Science Applications (65 citations). David N. Chin has collaborated with scholars based in United States, Taiwan and Hong Kong. Frequent co-authors include Robert Wilensky, Yigal Arens, Alex Quilici, Alfred Kobsa, Karen Spärck Jones, Marlene Jones, Robin Cohen, A. Jameson, Tim Finin and Robert Kass. Their work appears in journals such as Communications of the ACM, Artificial Intelligence Review and Computational Linguistics.
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.