Social interactions model and adaptability of human behavior.

Area of Science:

• Social network dynamics
• Computational social science
• Network science

Background:

• Human social networks are dynamic, influenced by both in-person and technology-mediated interactions.
• These evolving network structures significantly impact the behavior of dynamical processes within them.
• Existing models often struggle to capture the diverse temporal characteristics of social interactions.

Purpose of the Study:

• To develop a general, adaptable model for pairwise human social interactions.
• To analyze the distribution of social interaction durations within this model.
• To provide a tool for simulating processes on dynamic social networks.

Main Methods:

• Development of a general model for pairwise human social interaction.
• Analysis of interaction duration distributions under different model parameters.
• Algorithmic implementation for simulation of dynamical processes.

Main Results:

• The model demonstrates that interaction duration distributions can follow a power-law or a Weibull function depending on parameter values.
• This adaptability allows the model to represent both face-to-face (fat-tailed) and mobile-phone (Weibull) communication data.
• The model offers a simple algorithmic implementation for simulations.

Conclusions:

• The proposed adaptable social interaction model effectively captures diverse interaction duration patterns.
• This model is valuable for simulating dynamical processes in evolving social networks.
• It bridges the gap between modeling face-to-face and technology-mediated human interactions.