Peter Sin

43 papers receiving 222 citations

Peers

Peter Sin
Comparison fields: 5 of 30
  • Discrete Mathematics and Combinatorics 175
  • Geometry and Topology 99
  • Mathematical Physics 88
  • Algebra and Number Theory 37
  • Artificial Intelligence 107
Replace Kok Bin Wong with:
Kok Bin Wong Malaysia
Sung‐Yell Song United States
Patrizia Longobardi Italy
Mercede Maj Italy
Norbert Seifter Austria
Wolfgang Kimmerle Germany
Satoshi Yoshiara Japan
Frank Lübeck Germany
Robert A. Liebler United States
Alberto Del Fra Italy
Peter Sin relative to Kok Bin Wong Malaysia Kok Bin Wong's profile →
Citations per field
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Kok Bin Wong · 1×
Citations per year

Countries citing papers authored by Peter Sin

Since Specialization
Citations

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

Fields of papers citing papers by Peter Sin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Peter Sin, 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 Peter Sin Line = papers co-authored together Peter Sin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200030
2 200519
3 199613
4 201412
5 201012
6 200610
7 20039
8 20189
9 20108
10 19928
11 20007
12 20066
13 19946
14 20016
15 19926
16 19926
17 20015
18 20105
19 20185
20 19965

About Peter Sin

Peter Sin is a scholar working on Discrete Mathematics and Combinatorics, Artificial Intelligence, Geometry and Topology, Mathematical Physics and Algebra and Number Theory, having authored 48 papers that have together received 248 indexed citations. Recurring topics across this work include Finite Group Theory Research (31 papers), Coding theory and cryptography (22 papers), Advanced Algebra and Geometry (17 papers), Advanced Topics in Algebra (10 papers), Graph theory and applications (8 papers), graph theory and CDMA systems (8 papers), Algebraic structures and combinatorial models (7 papers) and Cooperative Communication and Network Coding (4 papers). The work is most often cited by research in Discrete Mathematics and Combinatorics (175 citations), Geometry and Topology (99 citations), Mathematical Physics (88 citations), Algebra and Number Theory (37 citations) and Artificial Intelligence (107 citations). Peter Sin has collaborated with scholars based in United States, India and Taiwan. Frequent co-authors include Qing Xiang, Pham Huu Tiep, Michael F. Dowd, Karen Meagher, Yuwen Chen, Asoo J. Vakharia, Janice E. Carrillo, John G. Thompson, Линг Лонг and Junhua Wu. Their work appears in journals such as Journal of Algebra, Journal of Combinatorial Theory Series A, Journal of Algebraic Combinatorics, Proceedings of the London Mathematical Society and Decision Sciences.

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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