Abstract

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

Original languageEnglish (US)
Pages (from-to)1433-1438
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number7
DOIs
StatePublished - Feb 13 2018

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Social Support
Communication
Interpersonal Relations
Cell Phones
Bees
Internet
Demography
Technology
Health
Population

Keywords

  • Trophallaxis
  • barcode
  • burstiness
  • temporal network
  • tracking

ASJC Scopus subject areas

  • General

Cite this

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title = "Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks",
abstract = "Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.",
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author = "Tim Gernat and Rao, {Vikyath D.} and Martin Middendorf and Harry Dankowicz and Nigel Goldenfeld and Robinson, {Gene E.}",
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AU - Rao, Vikyath D.

AU - Middendorf, Martin

AU - Dankowicz, Harry

AU - Goldenfeld, Nigel

AU - Robinson, Gene E.

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