Stephen Dill
Impact in
- Information Systems top 2%
- Web Data Mining and Analysis
- Service-Oriented Architecture and Web Services
- Artificial Intelligence top 5%
- Semantic Web and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Web Data Mining and Analysis 4
- Spam and Phishing Detection 2
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- Semantic Web and Ontologies 4
- Co-authors
- Andrew Tomkins (6 shared papers)Sridhar Rajagopalan (6 shared papers)Daniel Gruhl (5 shared papers)John A. Tomlin (4 shared papers)Anant Jhingran (4 shared papers)Nadav Eiron (4 shared papers)Jason Y. Zien (4 shared papers)David Gibson (4 shared papers)
- Journals
- ACM Transactions on Internet Technology (1 paper)Journal of Web Semantics (1 paper)Lecture notes in computer science (1 paper)Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (1 paper)Very Large Data Bases (1 paper)
- Partner nations
- United StatesIndiaArgentina
In The Last Decade
Stephen Dill
16 papers receiving 698 citations
Peers
Comparison fields: 5 of 61
- Information Systems 489
- Artificial Intelligence 505
- Statistical and Nonlinear Physics 117
- Management Science and Operations Research 108
- Computer Networks and Communications 195
Countries citing papers authored by Stephen Dill
This map shows the geographic impact of Stephen Dill'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 Stephen Dill with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Dill more than expected).
Fields of papers citing papers by Stephen Dill
This network shows the impact of papers produced by Stephen Dill. 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 Stephen Dill. The network helps show where Stephen Dill may publish in the future.
Co-authors
The 25 scholars most cited alongside Stephen Dill, 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 | 2003 | 368 | |
| 2 | 2002 | 133 | |
| 3 | 2003 | 105 | |
| 4 | 2003 | 64 | |
| 5 | Self-similarity in the Web | 2001 | 52 |
| 6 | 2013 | 15 | |
| 7 | 2009 | 15 | |
| 8 | 2006 | 12 | |
| 9 | 2014 | 12 | |
| 10 | 2009 | 7 | |
| 11 | 2009 | 5 | |
| 12 | A CRM system for Social Media | 2013 | 3 |
| 13 | 2003 | 2 | |
| 14 | 2013 | 2 | |
| 15 | 2010 | 1 | |
| 16 | 2009 | 1 |
About Stephen Dill
Stephen Dill is a scholar working on Information Systems, Artificial Intelligence, Statistical and Nonlinear Physics, Information Systems and Management and Management Science and Operations Research, having authored 16 papers that have together received 797 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (4 papers), Semantic Web and Ontologies (4 papers), Data Quality and Management (4 papers), Complex Network Analysis Techniques (4 papers), Spam and Phishing Detection (2 papers), Business Process Modeling and Analysis (2 papers), Theoretical and Computational Physics (2 papers) and Team Dynamics and Performance (2 papers). The work is most often cited by research in Information Systems (489 citations), Artificial Intelligence (505 citations), Statistical and Nonlinear Physics (117 citations), Management Science and Operations Research (108 citations) and Computer Networks and Communications (195 citations). Stephen Dill has collaborated with scholars based in United States, India and Argentina. Frequent co-authors include Andrew Tomkins, Sridhar Rajagopalan, Daniel Gruhl, John A. Tomlin, Anant Jhingran, Nadav Eiron, Jason Y. Zien, David Gibson, Tapas Kanungo and R. Guha. Their work appears in journals such as ACM Transactions on Internet Technology, Journal of Web Semantics, Lecture notes in computer science, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing and Very Large Data Bases.
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.