System of mobile agents to model social networks

Area of Science:

• Computational Social Science
• Network Science
• Statistical Physics

Background:

• Empirical friendship networks exhibit complex structural properties.
• Existing models often struggle to capture community structure and emergent phenomena.

Purpose of the Study:

• To develop a novel agent-based model for social network construction.
• To reproduce key topological features of empirical social networks.
• To investigate emergent network properties and their relation to agent interaction dynamics.

Main Methods:

• A model of mobile agents simulating particle collisions to build networks.
• Analysis of network metrics including degree distribution, clustering coefficient, and shortest path length.
• Examination of community structure and emergent phenomena like giant clusters.

Main Results:

• The model successfully reproduces empirical friendship network characteristics.
• Network formation is governed by a collision rate parameter.
• Emergence of a giant cluster above a critical collision rate, consistent with 2D percolation universality class.

Conclusions:

• Agent-based modeling with collision dynamics offers a robust framework for social network generation.
• The collision rate is a critical parameter determining network topology and emergent structures.
• The model shows potential for simulating diverse social contact networks, including sexual contacts.