GA-S^3 : Comprehensive Social Network Simulation with Group Agents (2025)

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 S ocial network S imulation S ystem ( GA\text{-}S^3 ) that leverages newly designed G roup A gents 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.

## introduction online social networks have emerged as primary platforms for social activities, where users engage in different interactive behaviors, including chatting, posting, and sharing content. social network simulations @xcite create virtual representations of social networks, modeling the behaviors, relationships, and information flows among individuals or entities within these networks. the goal of social network simulation @xcite is to analyze and predict the outcomes that emerge from these interactions, and can be leveraged for a wide range of applications such as group behavior * corresponding author emergence, phenomenon prediction, policy optimization, and business strategy development. traditional social network simulation systems predominantly adhere to discrete events @xcite or system dynamics @xcite focusing on predicting variables rather than elucidating underlying mechanisms or causal relationships. they often underestimate the heterogeneity of social behavior and the dynamism of social structures. recently, research @xcite @xcite tends on social networks simulation focus on employing the large language model (llm) @xcite @xcite based agents to simulate individual entities and their behaviors. however, with billions of users on social networks, it is impossible to model each user individually through llmbased agents @xcite @xcite . additionally, existing social simulation systems suffer from poor scalability and are frequently designed for particular events, thereby failing to comprehensively cover news events on the internet. to address the challenges of high complexity arising from billions of individuals and the poor scalability of social network simulations, we propose a comprehensive social network simulation system (ga-s 3 ) based on the intelligent agents-group agents. group agents are designed to represent collections of individuals with similar online behaviors, rather than simulating each individual entity, thereby enabling the modeling of complex interactions at a manageable computational cost. moreover, our group agents can adaptively generate user profiles based on network events, ensuring scalability across diverse online environments. our group agents consist of three primary modules: hierarchical generation, decision-reasoning, and action. these modules correspond to the three stages of the agents' life-cycle: existence, decisionmaking, and behavior. in the hierarchical generation, group agents' profiles are created based on the environmental event, including population, identity and characteristics. groups are progressively differentiated within a hierarchical tree structure until they accurately represent populations with similar behaviors. in the decision-reasoning, agents make the decision based on their role and the propagation of states and memory. we incorporate finegrained factors such as emotion fading and forgetting probability to ensure alignment with realworld dynamics. in the action, our agents emulate human actions on the internet and conduct interactions among multiple groups. we developed a social network benchmark that compiles popular online events from 2024 across various domains and includes detailed information on fine-grained network traffic variations. experimental results demonstrate that our ga-s 3 model achieves accurate and highly realistic predictions. ## methodology our comprehensive social network simulation system (ga-s 3 ) is shown in figure 1 , the group agents comprise three key modules: hierarchical generation, decision-reasoning, and action. the hierarchical generation is designed to automatically construct group agents based on the given network environmental events, shaping their perception from both the environment and content. the decisionreasoning, functioning as the agent's core and akin to a human brain, simulates human-like decisionmaking by reasoning about perceived content in alignment with role attributes. the action simulates human behavior and enables the interaction with network entities or other agents according to decisions. ## conclusion in this study, we introduce a comprehensive social network simulation framework based on group agents. this framework effectively constructs virtual representations of social networks and simulates human behaviors. we have designed group agents capable of modeling a collective of individuals and engaging in social network interactions. the group agents incorporate hierarchical generation, decision-reasoning, and action modules corresponding to the three stages in the life-cycle of existence, decision, and behavior. this design enables the simulation of large-scale network phenomena with complex interactions while maintain-ing a manageable computational cost. experiments conducted on our bns benchmark demonstrate that our ga-s 3 system delivers accurate and realistic predictive outcomes.