Kate Larson

98 papers receiving 1.9k citations

Kate Larson's Hit Papers

Coalition structure generation with worst case guarantees 1999 · 527 citations
5270+9+18Years since publication100200300400500

Peers

Kate Larson
Comparison fields: 5 of 112
  • Management Science and Operations Research 945
  • Computer Science Applications 143
  • Artificial Intelligence 739
  • Health Informatics 26
  • Economics and Econometrics 520
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Citations per year

Countries citing papers authored by Kate Larson

Since Specialization
Citations

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

Fields of papers citing papers by Kate Larson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 103 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Coalition structure generation with worst case guarantees
Hit paper breakdown →
1999527
2
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems
2017168
3 2021151
4
Costly valuation computation in auctions
200173
5 200055
6 200151
7 199848
8 201842
9 200942
10
A characterisation of strategy-proofness for grounded argumentation semantics
200941
11 200841
12 201935
13 202033
14
Pareto optimality in abstract argumentation
200831
15 201429
16 200229
17 201127
18 201926
19 201925
20
Coalitional affinity games and the stability gap
200924

About Kate Larson

Kate Larson is a scholar working on Management Science and Operations Research, Economics and Econometrics, Artificial Intelligence, Safety Research and Computer Networks and Communications, having authored 103 papers that have together received 2.0k indexed citations. Recurring topics across this work include Auction Theory and Applications (54 papers), Game Theory and Voting Systems (37 papers), Game Theory and Applications (33 papers), Experimental Behavioral Economics Studies (15 papers), Multi-Agent Systems and Negotiation (9 papers), Mobile Crowdsensing and Crowdsourcing (9 papers), Optimization and Search Problems (8 papers) and Decision-Making and Behavioral Economics (7 papers). The work is most often cited by research in Management Science and Operations Research (945 citations), Computer Science Applications (143 citations), Artificial Intelligence (739 citations), Health Informatics (26 citations) and Economics and Econometrics (520 citations). Kate Larson has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Tüomas Sandholm, Fernando Tohmé, Martin Andersson, Onn Shehory, Iyad Rahwan, Michael Winikoff, Edmund H. Durfee, Sanmay Das, Edith Law and Simina Brânzei. Their work appears in journals such as Artificial Intelligence, Autonomous Agents and Multi-Agent Systems, Proceedings of the ACM on Human-Computer Interaction, Theory of Computing Systems and Decision.

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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