Rick Barber
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
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- Web Data Mining and Analysis 4
- Web visibility and informetrics 2
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- Advanced Database Systems and Queries 2
- Caching and Content Delivery 1
- Co-authors
- Jiawei Han (2 shared papers)Charų C. Aggarwal (1 shared paper)Yizhou Sun (1 shared paper)Alex Kirlik (1 shared paper)Karrie Karahalios (1 shared paper)Joon-Sung Park (1 shared paper)Fabio Fumarola (4 shared papers)Tim Weninger (4 shared papers)
- Journals
- Proceedings of the ACM on Human-Computer Interaction (1 paper)ACM SIGKDD Explorations Newsletter (1 paper)CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (2 papers)Conference on Innovative Data Systems Research (1 paper)
- Partner nations
- United StatesItaly
In The Last Decade
Rick Barber
7 papers receiving 358 citations
Peers
Comparison fields: 5 of 58
- Statistical and Nonlinear Physics 171
- Artificial Intelligence 238
- Information Systems 125
- General Decision Sciences 10
- Computer Science Applications 16
Countries citing papers authored by Rick Barber
This map shows the geographic impact of Rick Barber'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 Rick Barber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rick Barber more than expected).
Fields of papers citing papers by Rick Barber
This network shows the impact of papers produced by Rick Barber. 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 Rick Barber. The network helps show where Rick Barber may publish in the future.
Co-authors
The 13 scholars most cited alongside Rick Barber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 284 | |
| 2 | 2019 | 40 | |
| 3 | 2011 | 14 | |
| 4 | Arnold: Declarative Crowd-Machine Data Integration | 2013 | 11 |
| 5 | 2011 | 9 | |
| 6 | 2011 | 7 | |
| 7 | 2011 | 4 |
About Rick Barber
Rick Barber is a scholar working on Information Systems, Computer Networks and Communications, Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 369 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (4 papers), Web visibility and informetrics (2 papers), Data Quality and Management (2 papers), Advanced Database Systems and Queries (2 papers), Complex Network Analysis Techniques (1 paper), Data Management and Algorithms (1 paper), Caching and Content Delivery (1 paper) and Ethics and Social Impacts of AI (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (171 citations), Artificial Intelligence (238 citations), Information Systems (125 citations), General Decision Sciences (10 citations) and Computer Science Applications (16 citations). Rick Barber has collaborated with scholars based in United States and Italy. Frequent co-authors include Jiawei Han, Charų C. Aggarwal, Yizhou Sun, Alex Kirlik, Karrie Karahalios, Joon-Sung Park, Fabio Fumarola, Tim Weninger, Donato Malerba and Jiawei Han. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, ACM SIGKDD Explorations Newsletter, CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) and Conference on Innovative Data Systems Research.
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.