Javier Navaridas

1.2k citations
73 papers · 740 · h-index 16

Impact in

Papers in

Javier Navaridas

71 papers receiving 712 citations

Peers

Javier Navaridas
Comparison fields: 5 of 42
  • Hardware and Architecture 230
  • Computer Networks and Communications 401
  • Electrical and Electronic Engineering 403
  • Cellular and Molecular Neuroscience 74
  • Artificial Intelligence 126
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Citations per field
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Citations per year

Countries citing papers authored by Javier Navaridas

Since Specialization
Citations

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

Fields of papers citing papers by Javier Navaridas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Javier Navaridas, 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 Javier Navaridas Line = papers co-authored together Javier Navaridas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201794
2 201738
3 201034
4 200934
5 201433
6 201627
7 201027
8 201523
9 201522
10 201922
11 200922
12 200820
13 202319
14 202217
15 200717
16 202215
17 201214
18 201714
19 202513
20 201512

About Javier Navaridas

Javier Navaridas is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Hardware and Architecture, Cellular and Molecular Neuroscience and Information Systems, having authored 73 papers that have together received 740 indexed citations. Recurring topics across this work include Interconnection Networks and Systems (40 papers), Parallel Computing and Optimization Techniques (30 papers), Advanced Memory and Neural Computing (16 papers), Photonic and Optical Devices (11 papers), Neuroscience and Neural Engineering (11 papers), Embedded Systems Design Techniques (10 papers), Cloud Computing and Resource Management (9 papers) and Optical Network Technologies (8 papers). The work is most often cited by research in Hardware and Architecture (230 citations), Computer Networks and Communications (401 citations), Electrical and Electronic Engineering (403 citations), Cellular and Molecular Neuroscience (74 citations) and Artificial Intelligence (126 citations). Javier Navaridas has collaborated with scholars based in United Kingdom, Spain and China. Frequent co-authors include Mikel Luján, Sebastian Werner, José Miguel-Alonso, José A. Pascual, Steve Furber, Luis A. Plana, John Goodacre, Wei Song, Geoffrey Ndu and Iain A. Stewart. Their work appears in journals such as Parallel Computing, Simulation Modelling Practice and Theory, IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing and ACM Computing Surveys.

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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