Jean-Philippe Vasseur

21 papers receiving 1.0k citations

Peers

Jean-Philippe Vasseur
Comparison fields: 5 of 59
  • Computer Networks and Communications 900
  • Electrical and Electronic Engineering 553
  • Information Systems 89
  • Artificial Intelligence 71
  • Computer Vision and Pattern Recognition 68
Replace Emmanuel Baccelli with:
Emmanuel Baccelli France
Peter Martini Germany
Yoshiaki Kakuda Japan
Radek Fujdiak Czechia
Zizhan Zheng United States
H.S.M. Zedan United Kingdom
Hans-Peter Schwefel Denmark
Sheng-Tzong Cheng Taiwan
Shikhar Verma Japan
Petr Mlýnek Czechia
Jean-Philippe Vasseur relative to Emmanuel Baccelli France Emmanuel Baccelli's profile →
Citations per field
00.5×1.5×1.9×
Emmanuel Baccelli · 1×
Citations per year

Countries citing papers authored by Jean-Philippe Vasseur

Since Specialization
Citations

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

Fields of papers citing papers by Jean-Philippe Vasseur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean-Philippe Vasseur. 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 Jean-Philippe Vasseur. The network helps show where Jean-Philippe Vasseur may publish in the future.

Co-authorship network of co-authors of Jean-Philippe Vasseur

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 25
2
Beyond Interoperability - Pushing the Performance of Sensor Network IP Stacks
21
3 45
4 1
5 38
6 106
7
Interconnecting Smart Objects with IP: The Next Internet
259
8 2
9 7
10 4
11 4
12 53
13 5
14 17
15
Definitive MPLS Network Designs
39
16 149
17
Network Recovery: Protection and Restoration of Optical, SONET-SDH, IP, and MPLS
266
18 0
19 18
20
The IP/MPLS over ASON/GMPLS test-bed of the IST Project LION
1

About Jean-Philippe Vasseur

Jean-Philippe Vasseur is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Software, having authored 22 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Optical Network Technologies (13 papers), Software-Defined Networks and 5G (7 papers) and Optical Network Technologies (7 papers). The work is most often cited by research in Computer Networks and Communications (900 citations), Electrical and Electronic Engineering (553 citations) and Software (24 citations). Jean-Philippe Vasseur has collaborated with scholars based in United States, Italy and Czechia. Frequent co-authors include Adam Dunkels, Mario Pickavet, Piet Demeester, Dina Katabi, Srikanth Kandula, Joseph P. Olive, Jaudelice C. de Oliveira, François Le Faucheur, Mário Gerla and Andreas Terzis. Their work appears in journals such as IEEE Communications Magazine, Journal of Lightwave Technology and Computer Networks.

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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