Kevin Wayne

656 citations
31 papers · 435 · h-index 14

Impact in

Papers in

Kevin Wayne

29 papers receiving 424 citations

Peers

Kevin Wayne
Comparison fields: 5 of 74
  • Urology 45
  • Computational Theory and Mathematics 123
  • Psychiatry and Mental health 59
  • Computer Science Applications 25
  • Computer Networks and Communications 104
Replace Mauro Gáspari with:
Mauro Gáspari Italy
Stefania Costantini Italy
Ákos Kiss Hungary
Will Ma United States
Jeongsim Kim South Korea
Ah Reum Kang South Korea
Maurizio Atzori Italy
Gürkan Öztürk Türkiye
Danyi Li China
Lothar Gierl Germany
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Citations per field
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Citations per year

Countries citing papers authored by Kevin Wayne

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Wayne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Wayne. 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 Kevin Wayne. The network helps show where Kevin Wayne may publish in the future.

Co-authors

The 25 scholars most cited alongside Kevin Wayne, 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 Kevin Wayne Line = papers co-authored together Kevin Wayne links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200252
2 200247
3 199833
4 201832
5
Generalized maximum flow algorithms
199932
6 201630
7 201824
8
Introduction to Programming in Python: An Interdisciplinary Approach
200724
9 201722
10 199922
11 200116
12 201815
13
Faster approximation algorithms for generalized flow
199914
14 201914
15 201812
16
Computer Science: An Interdisciplinary Approach
201612
17 20125
18 20204
19 20214
20 20213

About Kevin Wayne

Kevin Wayne is a scholar working on Computational Theory and Mathematics, Surgery, Computer Networks and Communications, Computer Science Applications and Computer Graphics and Computer-Aided Design, having authored 31 papers that have together received 435 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (7 papers), Bladder and Urothelial Cancer Treatments (6 papers), Advanced Graph Theory Research (4 papers), Teaching and Learning Programming (3 papers), Spreadsheets and End-User Computing (2 papers), Constraint Satisfaction and Optimization (2 papers), Advanced Algebra and Logic (2 papers) and Sports Analytics and Performance (2 papers). The work is most often cited by research in Urology (45 citations), Computational Theory and Mathematics (123 citations), Psychiatry and Mental health (59 citations), Computer Science Applications (25 citations) and Computer Networks and Communications (104 citations). Kevin Wayne has collaborated with scholars based in United States, Canada and Finland. Frequent co-authors include Éva Tardos, Lisa Fleischer, Robert Sedgewick, Jeffrey Loh-Doyle, Stuart D. Boyd, Mukul Patil, Hooman Djaladat, Siamak Daneshmand, Thomas G. Clifford and Anne Schuckman. Their work appears in journals such as Urology, The Journal of Sexual Medicine, The Journal of Urology, Urologic Oncology Seminars and Original Investigations and Neurourology and Urodynamics.

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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