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 5
- Web visibility and informetrics 2
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- Advanced Database Systems and Queries 2
- IoT and Edge/Fog Computing 1
- Co-authors
- Jiawei Han (2 shared papers)Yizhou Sun (1 shared paper)Charų C. Aggarwal (1 shared paper)Joon-Sung Park (1 shared paper)Alex Kirlik (1 shared paper)Karrie G. Karahalios (1 shared paper)Tim Weninger (5 shared papers)Fabio Fumarola (5 shared papers)
- Journals
- Proceedings of the ACM on Human-Computer Interaction (1 paper)Lecture notes in computer science (1 paper)Conference on Innovative Data Systems Research (1 paper)CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (2 papers)ACM SIGKDD Explorations Newsletter (1 paper)
- Partner nations
- United StatesItaly
In The Last Decade
Rick Barber
8 papers receiving 451 citations
Rick Barber's Hit Papers
Peers
Comparison fields: 5 of 59
- Statistical and Nonlinear Physics 212
- Artificial Intelligence 302
- Information Systems 167
- General Decision Sciences 11
- Health Informatics 6
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 | Co-author Relationship Prediction in Heterogeneous Bibliographic Networks Hit paper breakdown → | 2011 | 337 |
| 2 | 2019 | 55 | |
| 3 | 2011 | 17 | |
| 4 | 2011 | 14 | |
| 5 | 2011 | 13 | |
| 6 | Arnold: Declarative Crowd-Machine Data Integration | 2013 | 13 |
| 7 | 2011 | 9 | |
| 8 | 2011 | 6 |
About Rick Barber
Rick Barber is a scholar working on Information Systems, Computer Networks and Communications, Management Science and Operations Research, Artificial Intelligence and Computer Science Applications, having authored 8 papers that have together received 464 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (5 papers), Web visibility and informetrics (2 papers), Data Quality and Management (2 papers), Advanced Database Systems and Queries (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Ethics and Social Impacts of AI (1 paper), IoT and Edge/Fog Computing (1 paper) and Innovative Human-Technology Interaction (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (212 citations), Artificial Intelligence (302 citations), Information Systems (167 citations), General Decision Sciences (11 citations) and Health Informatics (6 citations). Rick Barber has collaborated with scholars based in United States and Italy. Frequent co-authors include Jiawei Han, Yizhou Sun, Charų C. Aggarwal, Joon-Sung Park, Alex Kirlik, Karrie G. Karahalios, Tim Weninger, Fabio Fumarola, Donato Malerba and Jiawei Han. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Lecture notes in computer science, Conference on Innovative Data Systems Research, CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) and ACM SIGKDD Explorations Newsletter.
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.