Crowd: A Social Network Simulation Framework

TL;DR

Crowd is a Python-based social network simulation framework that simplifies modeling complex social phenomena with features like no-code setup, visualizations, and easy integration with data analysis tools.

Contribution

It introduces a flexible, user-friendly social network simulator with customizable features, supporting generative agents and real-world phenomena modeling.

Findings

Demonstrated use cases in epidemics, influence maximization, and trust games.

Enhanced ease of modeling social network dynamics.

Facilitated data analysis and visualization.

Abstract

To observe how individual behavior shapes a larger community's actions, agent-based modeling and simulation (ABMS) has been widely adopted by researchers in social sciences, economics, and epidemiology. While simulations can be run on general-purpose ABMS frameworks, these tools are not specifically designed for social networks and, therefore, provide limited features, increasing the effort required for complex simulations. In this paper, we introduce Crowd, a social network simulator that adopts the agent-based modeling methodology to model real-world phenomena within a network environment. Designed to facilitate easy and quick modeling, Crowd supports simulation setup through YAML configuration and enables further customization with user-defined methods. Other features include no-code simulations for diffusion tasks, interactive visualizations, data aggregation, and chart drawing facilities. Designed in Python, Crowd also supports generative agents and connects easily with Python's libraries for data analysis and machine learning. Finally, we include three case studies to illustrate the use of the framework, including generative agents in epidemics, influence maximization, and networked trust games.