Participation shifts explain degree distributions in a human communications network.
Human interpersonal communications drive political, technological, and economic systems, placing importance on network link prediction as a fundamental problem of the sciences. These systems are often described at the network-level by degree counts -the number of communication links associated with...
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doaj-893a0cf8f3bb408a9dfb129b66106c042021-03-03T21:35:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01145e021724010.1371/journal.pone.0217240Participation shifts explain degree distributions in a human communications network.C Ben GibsonNorbou BuchlerBlaine HoffmanClaire-Genevieve La FleurHuman interpersonal communications drive political, technological, and economic systems, placing importance on network link prediction as a fundamental problem of the sciences. These systems are often described at the network-level by degree counts -the number of communication links associated with individuals in the network-that often follow approximate Pareto distributions, a divergence from Poisson-distributed counts associated with random chance. A defining challenge is to understand the inter-personal dynamics that give rise to such heavy-tailed degree distributions at the network-level; primarily, these distributions are explained by preferential attachment, which, under certain conditions, can create power law distributions; preferential attachment's prediction of these distributions breaks down, however, in conditions with no network growth. Analysis of an organization's email network suggests that these degree distributions may be caused by the existence of individual participation-shift dynamics that are necessary for coherent communication between humans. We find that the email network's degree distribution is best explained by turn-taking and turn-continuing norms present in most social network communication. We thus describe a mechanism to explain a long-tailed degree distribution in conditions with no network growth.https://doi.org/10.1371/journal.pone.0217240 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C Ben Gibson Norbou Buchler Blaine Hoffman Claire-Genevieve La Fleur |
spellingShingle |
C Ben Gibson Norbou Buchler Blaine Hoffman Claire-Genevieve La Fleur Participation shifts explain degree distributions in a human communications network. PLoS ONE |
author_facet |
C Ben Gibson Norbou Buchler Blaine Hoffman Claire-Genevieve La Fleur |
author_sort |
C Ben Gibson |
title |
Participation shifts explain degree distributions in a human communications network. |
title_short |
Participation shifts explain degree distributions in a human communications network. |
title_full |
Participation shifts explain degree distributions in a human communications network. |
title_fullStr |
Participation shifts explain degree distributions in a human communications network. |
title_full_unstemmed |
Participation shifts explain degree distributions in a human communications network. |
title_sort |
participation shifts explain degree distributions in a human communications network. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2019-01-01 |
description |
Human interpersonal communications drive political, technological, and economic systems, placing importance on network link prediction as a fundamental problem of the sciences. These systems are often described at the network-level by degree counts -the number of communication links associated with individuals in the network-that often follow approximate Pareto distributions, a divergence from Poisson-distributed counts associated with random chance. A defining challenge is to understand the inter-personal dynamics that give rise to such heavy-tailed degree distributions at the network-level; primarily, these distributions are explained by preferential attachment, which, under certain conditions, can create power law distributions; preferential attachment's prediction of these distributions breaks down, however, in conditions with no network growth. Analysis of an organization's email network suggests that these degree distributions may be caused by the existence of individual participation-shift dynamics that are necessary for coherent communication between humans. We find that the email network's degree distribution is best explained by turn-taking and turn-continuing norms present in most social network communication. We thus describe a mechanism to explain a long-tailed degree distribution in conditions with no network growth. |
url |
https://doi.org/10.1371/journal.pone.0217240 |
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