David Naylor

960 citations
26 papers · 543 · h-index 11

Impact in

Papers in

    • Caching and Content Delivery 12
    • Opportunistic and Delay-Tolerant Networks 4
    • Software-Defined Networks and 5G 4
    • Network Security and Intrusion Detection 3
    • Peer-to-Peer Network Technologies 2
    • Internet Traffic Analysis and Secure E-voting 5

David Naylor

23 papers receiving 509 citations

Peers

David Naylor
Comparison fields: 5 of 88
  • Computer Networks and Communications 321
  • Behavioral Neuroscience 29
  • Signal Processing 75
  • Artificial Intelligence 197
  • Computer Vision and Pattern Recognition 85
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Citations per year

Countries citing papers authored by David Naylor

Since Specialization
Citations

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

Fields of papers citing papers by David Naylor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014134
2 201571
3 201565
4 197344
5
Elementary and Middle School Social Studies
198739
6 198136
7 201531
8 201730
9 201425
10 201520
11 196514
12 20149
13 20145
14 20124
15 20143
16
Holidays, Cultural Diversity, and the Public Culture.
19932
17
The Right to Assemble.
19912
18 20002
19 20162
20
Values: Law-Related Education and the Elementary School Teacher.
19762

About David Naylor

David Naylor is a scholar working on Computer Networks and Communications, Artificial Intelligence, Law, Education and Political Science and International Relations, having authored 26 papers that have together received 543 indexed citations. Recurring topics across this work include Caching and Content Delivery (12 papers), Internet Traffic Analysis and Secure E-voting (5 papers), Opportunistic and Delay-Tolerant Networks (4 papers), Software-Defined Networks and 5G (4 papers), Legal Issues in Education (3 papers), Network Security and Intrusion Detection (3 papers), Peer-to-Peer Network Technologies (2 papers) and IPv6, Mobility, Handover, Networks, Security (2 papers). The work is most often cited by research in Computer Networks and Communications (321 citations), Behavioral Neuroscience (29 citations), Signal Processing (75 citations), Artificial Intelligence (197 citations) and Computer Vision and Pattern Recognition (85 citations). David Naylor has collaborated with scholars based in United States, South Korea and Spain. Frequent co-authors include Peter Steenkiste, Matthew K. Mukerjee, Konstantina Papagiannaki, Ilias Leontiadis, Richard A. Diem, Dongsu Han, Junchen Jiang, Marco Mellia, Alessandro Finamore and Yan Grunenberger. Their work appears in journals such as ACM SIGCOMM Computer Communication Review, Journal of Social Work Practice, The Journal of Psychology, The Journal of Cell Biology and Immunochemistry.

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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