Temporal stability in human interaction networks

Renato Fabbri, Ricardo Fabbri, Deborah Christina Antunes, Marilia Mello Pisani and Osvaldo Novais de Oliveira

Physica A: Statistical Mechanics and its Applications, 2017, vol. 486, issue C, 92-105

Abstract: This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös–Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, <15% of the vertices are hubs, 15%–45% are intermediary and >45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.

Keywords: Complex networks; Pattern recognition; Statistics; Social network analysis; Human typology (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:486:y:2017:i:c:p:92-105

DOI: 10.1016/j.physa.2017.04.109

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