Lior Seeman
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
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- Game Theory and Applications
Papers in
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- Game Theory and Applications 5
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- Optimization and Search Problems 2
- Co-authors
- Yaron Singer (3 shared papers)Joseph Y. Halpern (4 shared papers)Rafael Pass (5 shared papers)Aviad Rubinstein (2 shared papers)Christos H. Papadimitriou (1 shared paper)Ashwinkumar Badanidiyuru (1 shared paper)Sigal Oren (1 shared paper)Łucja Kot (1 shared paper)
- Journals
- Topics in Cognitive Science (1 paper)Games and Economic Behavior (1 paper)Proceedings of the VLDB Endowment (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesFrance
In The Last Decade
Lior Seeman
8 papers receiving 104 citations
Peers
Comparison fields: 5 of 26
- Statistical and Nonlinear Physics 74
- Management Science and Operations Research 37
- Computer Science Applications 7
- Computer Networks and Communications 28
- Computational Theory and Mathematics 16
Countries citing papers authored by Lior Seeman
This map shows the geographic impact of Lior Seeman'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 Lior Seeman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lior Seeman more than expected).
Fields of papers citing papers by Lior Seeman
This network shows the impact of papers produced by Lior Seeman. 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 Lior Seeman. The network helps show where Lior Seeman may publish in the future.
Co-authors
The 11 scholars most cited alongside Lior Seeman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 70 | |
| 2 | Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions | 2015 | 15 |
| 3 | 2014 | 8 | |
| 4 | 2015 | 6 | |
| 5 | 2014 | 4 | |
| 6 | 2016 | 2 | |
| 7 | 2012 | 1 | |
| 8 | 2019 | 1 | |
| 9 | 2021 | 0 |
About Lior Seeman
Lior Seeman is a scholar working on Management Science and Operations Research, Computer Networks and Communications, Computational Theory and Mathematics, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 9 papers that have together received 107 indexed citations. Recurring topics across this work include Game Theory and Applications (5 papers), Complexity and Algorithms in Graphs (4 papers), Complex Network Analysis Techniques (3 papers), Optimization and Search Problems (2 papers), Computability, Logic, AI Algorithms (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Experimental Behavioral Economics Studies (1 paper) and Cryptography and Data Security (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (74 citations), Management Science and Operations Research (37 citations), Computer Science Applications (7 citations), Computer Networks and Communications (28 citations) and Computational Theory and Mathematics (16 citations). Lior Seeman has collaborated with scholars based in United States and France. Frequent co-authors include Yaron Singer, Joseph Y. Halpern, Rafael Pass, Aviad Rubinstein, Christos H. Papadimitriou, Ashwinkumar Badanidiyuru, Sigal Oren, Łucja Kot, Joe Halpern and Konstantinos Mamouras. Their work appears in journals such as Topics in Cognitive Science, Games and Economic Behavior, Proceedings of the VLDB Endowment and Proceedings of the AAAI Conference on Artificial Intelligence.
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.