Petar Maymounkov
- Computer Networks and Communications top 10%
- Artificial Intelligence
- Computational Mechanics
- Computational Theory and Mathematics top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Sivan ToledoHaim AvronJonathan A. KelnerKeren Censor-HillelBernhard HaeuplerMatthew BrandAndreas F. MolischE. Glen Weyl
- Topics
- Optimization and Search Problems (3 papers)Complexity and Algorithms in Graphs (3 papers)Mobile Ad Hoc Networks (2 papers)
- Cited by
- Computational MathematicsComputer Networks and CommunicationsComputational Theory and Mathematics
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Petar Maymounkov
7 papers receiving 191 citations
Peers
Comparison fields: 5 of 42
- Computer Networks and Communications 102
- Artificial Intelligence 90
- Computational Mechanics 58
- Computational Theory and Mathematics 53
- Computer Vision and Pattern Recognition 21
Countries citing papers authored by Petar Maymounkov
This map shows the geographic impact of Petar Maymounkov'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 Petar Maymounkov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Petar Maymounkov more than expected).
Fields of papers citing papers by Petar Maymounkov
This network shows the impact of papers produced by Petar Maymounkov. 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 Petar Maymounkov. The network helps show where Petar Maymounkov may publish in the future.
Co-authorship network of co-authors of Petar Maymounkov
This figure shows the co-authorship network connecting the top 25 collaborators of Petar Maymounkov. A scholar is included among the top collaborators of Petar Maymounkov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Petar Maymounkov. Petar Maymounkov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 27 | |
| 4 | 8 | |
| 5 | 105 | |
| 6 | 3 | |
| 7 | A peer-to-peer information system based on the XOR metric | 72 |
About Petar Maymounkov
Petar Maymounkov is a scholar working on Computational Mathematics, Computer Graphics and Computer-Aided Design and Computational Theory and Mathematics, having authored 7 papers that have together received 217 indexed citations. Recurring topics across this work include Optimization and Search Problems (3 papers), Complexity and Algorithms in Graphs (3 papers) and Mobile Ad Hoc Networks (2 papers). The work is most often cited by research in Computational Mathematics (6 citations), Computer Networks and Communications (102 citations) and Computational Theory and Mathematics (53 citations). Petar Maymounkov has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Sivan Toledo, Haim Avron, Jonathan A. Kelner, Keren Censor-Hillel, Bernhard Haeupler, Matthew Brand, Andreas F. Molisch and E. Glen Weyl. Their work appears in journals such as SIAM Journal on Computing, SIAM Journal on Scientific Computing and Theoretical Computer Science.
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.