GA-S$^3$: Comprehensive Social Network Simulation with Group Agents

2025-06-04

AI Summary

Conventional agent-based simulation of large-scale social networks suffers from prohibitive computational overhead and struggles to capture dynamic interactions and event evolution among billion-scale users. Method: This paper proposes the Group Agent paradigm, treating behaviorally homogeneous user clusters as fundamental simulation units. Integrated with a fine-grained, hot-event-driven benchmark dataset (constructed in 2024), multi-scale traffic dynamics modeling, and real-world Internet traffic calibration, it establishes an efficient, scalable, event-driven simulation framework. Contribution/Results: The approach overcomes key bottlenecks of traditional individual-agent modeling, significantly improving predictive fidelity for emergent collective behavior and policy intervention outcomes in real-world event forecasting tasks. The implementation is publicly available as open-source software.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Social network simulation is developed to provide a comprehensive understanding of social networks in the real world, which can be leveraged for a wide range of applications such as group behavior emergence, policy optimization, and business strategy development. However, billions of individuals and their evolving interactions involved in social networks pose challenges in accurately reflecting real-world complexities. In this study, we propose a comprehensive Social Network Simulation System (GA-S3) that leverages newly designed Group Agents to make intelligent decisions regarding various online events. Unlike other intelligent agents that represent an individual entity, our group agents model a collection of individuals exhibiting similar behaviors, facilitating the simulation of large-scale network phenomena with complex interactions at a manageable computational cost. Additionally, we have constructed a social network benchmark from 2024 popular online events that contains fine-grained information on Internet traffic variations. The experiment demonstrates that our approach is capable of achieving accurate and highly realistic prediction results. Code is open at https://github.com/AI4SS/GAS-3.

Problem

• Simulating large-scale social networks with complex interactions
• Reducing computational cost while maintaining realistic predictions
• Modeling group behaviors for accurate social network analysis

Innovation

• Group Agents model similar-behavior individuals
• Simulates large-scale network phenomena efficiently
• Social network benchmark from 2024 events