Peter Perešíni

1.1k total citations · 1 hit paper
18 papers, 627 citations indexed

About

Peter Perešíni is a scholar working on Computer Networks and Communications, Artificial Intelligence and Software. According to data from OpenAlex, Peter Perešíni has authored 18 papers receiving a total of 627 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Networks and Communications, 5 papers in Artificial Intelligence and 4 papers in Software. Recurrent topics in Peter Perešíni's work include Software-Defined Networks and 5G (14 papers), Network Security and Intrusion Detection (9 papers) and Software System Performance and Reliability (7 papers). Peter Perešíni is often cited by papers focused on Software-Defined Networks and 5G (14 papers), Network Security and Intrusion Detection (9 papers) and Software System Performance and Reliability (7 papers). Peter Perešíni collaborates with scholars based in Switzerland, Sweden and Spain. Peter Perešíni's co-authors include Dejan Kostić, Marco Canini, Maciej Kuźniar, Daniele Venzano, Jennifer Rexford, Nedeljko Vasić, Broňa Brejová, Tomáš Vinař and Vladimír Boža and has published in prestigious journals such as Bioinformatics, IEEE/ACM Transactions on Networking and ACM SIGCOMM Computer Communication Review.

In The Last Decade

Peter Perešíni

18 papers receiving 593 citations

Hit Papers

A NICE way to test openflow applications 2012 2026 2016 2021 2012 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A NICE way to test openflow applications
    2012 299 citations Marco Canini, Daniele Venzano et al. Infoscience (Ecole Polytechnique Fédérale de Lausanne) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peter Perešíni Switzerland 10 581 145 94 82 74 18 627
Maciej Kuźniar Switzerland 12 400 0.7× 110 0.8× 45 0.5× 33 0.4× 68 0.9× 18 423
Arup Abhinna Acharya India 11 326 0.6× 233 1.6× 30 0.3× 95 1.2× 15 0.2× 54 441
Teng Wang China 14 476 0.8× 55 0.4× 60 0.6× 42 0.5× 177 2.4× 41 568
Daniel Waddington United States 10 230 0.4× 70 0.5× 81 0.9× 23 0.3× 82 1.1× 32 304
Milenko Drinić United States 8 189 0.3× 44 0.3× 143 1.5× 86 1.0× 172 2.3× 19 341
Giuseppe Lettieri Italy 11 369 0.6× 52 0.4× 75 0.8× 21 0.3× 91 1.2× 43 432
John C. Gyllenhaal United States 13 389 0.7× 98 0.7× 144 1.5× 61 0.7× 575 7.8× 29 648
Andrea Calvagna Italy 10 124 0.2× 74 0.5× 37 0.4× 155 1.9× 23 0.3× 35 283
João Bispo Portugal 9 183 0.3× 29 0.2× 144 1.5× 38 0.5× 265 3.6× 51 351
Fangzhe Chang United States 12 315 0.5× 65 0.4× 71 0.8× 13 0.2× 94 1.3× 19 413

Countries citing papers authored by Peter Perešíni

Since Specialization
Citations

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

Fields of papers citing papers by Peter Perešíni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Perešíni

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Perešíni. A scholar is included among the top collaborators of Peter Perešíni 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 Peter Perešíni. Peter Perešíni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Boža, Vladimír, Peter Perešíni, Broňa Brejová, & Tomáš Vinař. (2020). DeepNano-blitz: a fast base caller for MinION nanopore sequencers. Bioinformatics. 36(14). 4191–4192. 15 indexed citations
2.
Kuźniar, Maciej, Peter Perešíni, Dejan Kostić, & Marco Canini. (2018). Methodology, measurement and analysis of flow table update characteristics in hardware openflow switches. Computer Networks. 136. 22–36. 37 indexed citations
3.
Perešíni, Peter, Maciej Kuźniar, & Dejan Kostić. (2018). Dynamic, Fine-Grained Data Plane Monitoring With Monocle. IEEE/ACM Transactions on Networking. 26(1). 534–547. 11 indexed citations
4.
Perešíni, Peter, Maciej Kuźniar, & Dejan Kostić. (2015). Rule-level Data Plane Monitoring With Monocle. ACM SIGCOMM Computer Communication Review. 45(4). 595–596. 3 indexed citations
5.
Perešíni, Peter, Maciej Kuźniar, Marco Canini, et al.. (2015). Systematically testing OpenFlow controller applications. Computer Networks. 92. 270–286. 1 indexed citations
6.
Perešíni, Peter, Maciej Kuźniar, & Dejan Kostić. (2015). Rule-level Data Plane Monitoring With Monocle. 595–596. 5 indexed citations
7.
Perešíni, Peter, Maciej Kuźniar, & Dejan Kostić. (2015). Monocle. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–13. 41 indexed citations
8.
Kuźniar, Maciej, Peter Perešíni, & Dejan Kostić. (2014). ProboScope: Data Plane Probe Packet Generation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 7 indexed citations
9.
Kuźniar, Maciej, Peter Perešíni, & Dejan Kostić. (2014). What you need to know about SDN control and data planes. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 16 indexed citations
10.
Perešíni, Peter, Maciej Kuźniar, Marco Canini, & Dejan Kostić. (2014). ESPRES: Easy Scheduling and Prioritization for SDN. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–2. 8 indexed citations
11.
Kuźniar, Maciej, Peter Perešíni, & Dejan Kostić. (2014). Providing Reliable FIB Update Acknowledgments in SDN. KTH Publication Database DiVA (KTH Royal Institute of Technology). 415–422. 32 indexed citations
12.
Perešíni, Peter, et al.. (2013). OF.CPP. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 97–102. 27 indexed citations
13.
Perešíni, Peter & Dejan Kostić. (2013). Is the Network Turing-Complete?. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
14.
Kuźniar, Maciej, Peter Perešíni, Nedeljko Vasić, Marco Canini, & Dejan Kostić. (2013). Automatic failure recovery for software-defined networks. KTH Publication Database DiVA (KTH Royal Institute of Technology). 159–160. 45 indexed citations
15.
Perešíni, Peter & Dejan Kostić. (2013). Is the network capable of computation?. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–6. 2 indexed citations
16.
Kuźniar, Maciej, Peter Perešíni, Marco Canini, Daniele Venzano, & Dejan Kostić. (2012). A SOFT way for openflow switch interoperability testing. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 265–276. 72 indexed citations
17.
Canini, Marco, Daniele Venzano, Peter Perešíni, Dejan Kostić, & Jennifer Rexford. (2012). A NICE way to test openflow applications. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 10–10. 299 indexed citations breakdown →
18.
Perešíni, Peter & Marco Canini. (2011). Is your OpenFlow application correct?. 1–2. 5 indexed citations

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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