Stephen G. Eick

79 papers receiving 2.2k citations

Peers

Stephen G. Eick
Comparison fields: 5 of 145
  • Computer Vision and Pattern Recognition 1.1k
  • Management Information Systems 303
  • Software 134
  • Signal Processing 323
  • Statistics and Probability 211
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Stephen G. Eick relative to Gary Kochenberger United States Gary Kochenberger's profile →
Citations per field
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Gary Kochenberger · 1×
Citations per year

Countries citing papers authored by Stephen G. Eick

Since Specialization
Citations

This map shows the geographic impact of Stephen G. Eick'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 G. Eick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen G. Eick more than expected).

Fields of papers citing papers by Stephen G. Eick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen G. Eick. 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 G. Eick. The network helps show where Stephen G. Eick may publish in the future.

Co-authors

The 25 scholars most cited alongside Stephen G. Eick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stephen G. Eick Line = papers co-authored together Stephen G. Eick links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1995315
2 1993192
3 1995140
4 1993135
5 1992127
6 199397
7 199794
8 199887
9 199485
10
Red light kills bacteria via photodynamic action.
200074
11 199666
12 200266
13 199565
14 199864
15 199558
16 199451
17 199751
18 200050
19 199343
20 199543

About Stephen G. Eick

Stephen G. Eick is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Information Systems and Sociology and Political Science, having authored 84 papers that have together received 2.5k indexed citations. Recurring topics across this work include Data Visualization and Analytics (48 papers), Multimedia Communication and Technology (12 papers), Video Analysis and Summarization (11 papers), Software Engineering Research (9 papers), Advanced Text Analysis Techniques (9 papers), Complex Network Analysis Techniques (9 papers), Data Management and Algorithms (8 papers) and Advanced Database Systems and Queries (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Management Information Systems (303 citations), Software (134 citations), Signal Processing (323 citations) and Statistics and Probability (211 citations). Stephen G. Eick has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Richard A. Becker, Ward Whitt, William A. Massey, Allan R. Wilks, Graham Wills, Nahum Gershon, Donald A. Berry, Kenneth C. Cox, Mei C. Chuah and Alan F. Karr. Their work appears in journals such as IEEE Computer Graphics and Applications, Journal of Computational and Graphical Statistics, Information Visualization, Communications of the ACM and IEEE Transactions on Visualization and Computer Graphics.

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.

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