Open AccessParticipation shifts explain degree distributions in a human communications network
Author(s) -
C. Ben Gibson,
Norbou Buchler,
Blaine Hoffman,
Claire-Genevieve La Fleur
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0217240
Subject(s) - degree distribution , degree (music) , preferential attachment , poisson distribution , pareto distribution , divergence (linguistics) , human dynamics , complex network , interpersonal communication , social network (sociolinguistics) , computer science , dynamic network analysis , telecommunications network , distribution (mathematics) , evolving networks , econometrics , mathematics , telecommunications , statistics , physics , computer network , artificial intelligence , social psychology , psychology , social media , linguistics , philosophy , world wide web , acoustics , mathematical analysis
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.