Ágnes Cseh

403 citations
25 papers · 169 · h-index 6

Impact in

Papers in

Ágnes Cseh

20 papers receiving 163 citations

Peers

Ágnes Cseh
Comparison fields: 5 of 47
  • Management Science and Operations Research 39
  • Psychiatry and Mental health 46
  • Economics and Econometrics 57
  • Cellular and Molecular Neuroscience 37
  • Clinical Psychology 40
Replace Dimitra Giannakopoulou with:
Dimitra Giannakopoulou Greece
L. Elliot Hong China
Leonardo Novelli Australia
Jue Xie Australia
Fábio P. Leite United States
Camilo Miguel Signorelli United Kingdom
Kelsey S. Montgomery United States
Klaus Oberauer Germany
Daniel Stamate United Kingdom
Charles Findling France
Ágnes Cseh relative to Dimitra Giannakopoulou Greece Dimitra Giannakopoulou's profile →
Citations per field
00.5×10×13×
Dimitra Giannakopoulou · 1×
Citations per year

Countries citing papers authored by Ágnes Cseh

Since Specialization
Citations

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

Fields of papers citing papers by Ágnes Cseh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200096
2 20179
3 20218
4 20178
5 20168
6 20177
7 20165
8 20194
9 20214
10 20133
11 20243
12 20223
13 20193
14 20232
15
Selected open problems in Matching Under Preferences
20191
16 20211
17 20221
18 20241
19 20221
20 20151

About Ágnes Cseh

Ágnes Cseh is a scholar working on Economics and Econometrics, Computational Theory and Mathematics, Management Science and Operations Research, Artificial Intelligence and Public Health, Environmental and Occupational Health, having authored 25 papers that have together received 169 indexed citations. Recurring topics across this work include Game Theory and Voting Systems (17 papers), Complexity and Algorithms in Graphs (12 papers), Auction Theory and Applications (10 papers), Game Theory and Applications (4 papers), Advanced Graph Theory Research (4 papers), Artificial Intelligence in Games (2 papers), Bipolar Disorder and Treatment (2 papers) and Organ Donation and Transplantation (2 papers). The work is most often cited by research in Management Science and Operations Research (39 citations), Psychiatry and Mental health (46 citations), Economics and Econometrics (57 citations), Cellular and Molecular Neuroscience (37 citations) and Clinical Psychology (40 citations). Ágnes Cseh has collaborated with scholars based in Hungary, Germany and United Kingdom. Frequent co-authors include David F. Manlove, Matti K. Karvonen, Takuya Saito, Pirkko Räsänen, Herbert M. Lachman, Jan Volavka, Matti Isohanni, Jari Tiihonen, Anu Putkonen and Pál Czobor. Their work appears in journals such as Games and Economic Behavior, Discrete Applied Mathematics, Journal of Quantitative Analysis in Sports, Theory of Computing Systems and International Journal of Game Theory.

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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