Participation shifts explain degree distributions in a human communications network

Area of Science:

• Network science
• Sociology
• Communication studies

Background:

• Human communication systems form complex networks driving political, technological, and economic progress.
• Network degree distributions, often heavy-tailed (Pareto), diverge from random (Poisson) models.
• Preferential attachment explains power-law distributions but fails in non-growing networks.

Purpose of the Study:

• To identify interpersonal dynamics causing heavy-tailed degree distributions in non-growing networks.
• To propose an alternative mechanism to preferential attachment for explaining network structure.
• To analyze organizational email networks for insights into communication patterns.

Main Methods:

• Analysis of an organization's email network data.
• Modeling degree distributions based on observed communication dynamics.
• Comparison of proposed mechanisms against preferential attachment theory.

Main Results:

• Organizational email network degree distribution is not explained by preferential attachment alone.
• Turn-taking and turn-continuing norms significantly explain the observed degree distribution.
• A novel mechanism for heavy-tailed distributions in static networks is identified.

Conclusions:

• Interpersonal communication norms, specifically turn-taking, are key drivers of network structure.
• This provides a mechanism for understanding heavy-tailed degree distributions without network growth.
• Findings offer insights into the fundamental dynamics of human interaction networks.