Daniel Granot

74 papers receiving 1.7k citations

Peers

Daniel Granot
Comparison fields: 5 of 83
  • Management Science and Operations Research 863
  • Management Information Systems 558
  • Economics and Econometrics 850
  • Strategy and Management 423
  • Marketing 246
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Muhittin Oral Canada
John H. Semple United States
Charles S. Tapiero United States
Joel Stutz United States
Renata Mansini Italy
Celik Parkan United States
Shiang-Tai Liu Taiwan
Jeremy F. Shapiro United States
Yanfei Lan China
Herbert Hamers Netherlands
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Citations per year

Countries citing papers authored by Daniel Granot

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Granot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1981178
2 2003145
3 2008121
4 2020111
5 198493
6 200779
7 200577
8 200575
9 198675
10 199153
11 199246
12 197742
13 199641
14 198241
15 199840
16 200638
17
Prior Solutions: Extensions of Convex Nucleus Solutions to Chance-Constrained Games.
197329
18 199325
19 199225
20 197623

About Daniel Granot

Daniel Granot is a scholar working on Economics and Econometrics, Management Science and Operations Research, Computational Theory and Mathematics, Industrial and Manufacturing Engineering and Management Information Systems, having authored 76 papers that have together received 1.8k indexed citations. Recurring topics across this work include Game Theory and Voting Systems (32 papers), Auction Theory and Applications (29 papers), Game Theory and Applications (19 papers), Vehicle Routing Optimization Methods (12 papers), Complexity and Algorithms in Graphs (10 papers), Supply Chain and Inventory Management (9 papers), Optimization and Packing Problems (8 papers) and Advanced Graph Theory Research (7 papers). The work is most often cited by research in Management Science and Operations Research (863 citations), Management Information Systems (558 citations), Economics and Econometrics (850 citations), Strategy and Management (423 citations) and Marketing (246 citations). Daniel Granot has collaborated with scholars based in Canada, United States and Israel. Frequent co-authors include Shuya Yin, Greys Sošić, Frieda Granot, Gur Huberman, Gur Huberman, A. Charnes, Michael Maschler, Dror Zuckerman, Shelby L. Brumelle and William Zhu. Their work appears in journals such as Management Science, International Journal of Game Theory, Operations Research, Discrete Applied Mathematics and Mathematical Programming.

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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