Scaling laws of human interaction activity

Area of Science:

• Social Network Analysis
• Statistical Physics
• Human Behavior

Background:

• Contemporary society relies heavily on communication, yet the fundamental laws governing human interaction remain poorly understood.
• Existing economic models, like Gibrat's law, explain growth patterns but lack a clear underlying principle.

Purpose of the Study:

• To investigate statistical laws governing human interaction activity within online social communities.
• To identify universal principles underlying communication patterns and their connection to economic dynamics.

Main Methods:

• Analysis of communication patterns in two distinct social Internet communities.
• Identification and mathematical formulation of scaling laws in human interaction activity.
• Investigation of long-term correlation patterns in human activity spanning over a year.

Main Results:

• Human communication networks dynamically follow generalized scaling laws, specifically a version of Gibrat's law for social activity.
• A scaling law was identified between message fluctuations and user activity levels.
• Long-term correlations (days to over a year) were found to underpin these scaling laws.

Conclusions:

• The identified scaling law in human communication suggests a universal mechanism potentially driving economic growth patterns (e.g., firm size, GDP, city growth).
• The findings provide a mathematical framework linking Gibrat's law to long-term correlated dynamics in human activity.
• These insights are crucial for designing effective communication networks and understanding social systems dynamics.