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

• URL: http://arxiv.org/abs/2506.03532v1
• Date: Wed, 04 Jun 2025 03:27:05 GMT
• Title: GA-S$^3$: Comprehensive Social Network Simulation with Group Agents
• Authors: Yunyao Zhang, Zikai Song, Hang Zhou, Wenfeng Ren, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang,
• Abstract summary: We propose a comprehensive Social Network Simulation System (GA-S3) that leverages newly designed Group Agents.<n>Unlike other intelligent agents that represent an individual entity, our group agents model a collection of individuals exhibiting similar behaviors.<n>We have constructed a social network benchmark from 2024 popular online events that contains fine-grained information on Internet traffic variations.
• Score: 24.44572534952312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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