# DynamiX: Large-Scale Dynamic Social Network Simulator

arXiv: https://arxiv.org/abs/2507.19929 · HTML: https://mlsanity.com/paper/2507.19929/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 71 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://mlsanity.com/paper/2411.11581/) (2024)
- [Line Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2010.10046/) (2020)
- [Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://mlsanity.com/paper/2402.16333/) (2024)
- [SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users](https://mlsanity.com/paper/2504.10157/) (2025)
- [From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News](https://mlsanity.com/paper/2403.09498/) (2024)
- [GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://mlsanity.com/paper/2410.04360/) (2024)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation](https://mlsanity.com/paper/2412.09237/) (2024)
- [YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models](https://mlsanity.com/paper/2505.07581/) (2025)
- [Computational Agent-based Models in Opinion Dynamics: A Survey on Social Simulations and Empirical Studies](https://mlsanity.com/paper/2306.03446/) (2023)
- [Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers](https://mlsanity.com/paper/1905.03919/) (2019)
- [A centrality measure for quantifying spread on weighted, directed networks](https://mlsanity.com/paper/2303.09684/) (2023)
- [TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System](https://mlsanity.com/paper/2412.12196/) (2024)
- [A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns](https://mlsanity.com/paper/2411.01143/) (2024)

## Built on by (most cited first, 2 of 2)

- [CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict](https://mlsanity.com/paper/2605.28369/) (2026)
- [Modeling U.S. Attitudes Toward China via an Event-Steered Multi-Agent Simulator](https://mlsanity.com/paper/2606.06971/) (2026)

## Abstract (arXiv)

Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated to dynamic social network modeling. DynamiX uses a dynamic hierarchy module for selecting core agents with key characteristics at each timestep, enabling accurate alignment of real-world adaptive switching of user roles. Furthermore, we design distinct dynamic social relationship modeling strategies for different user types. For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances, simulating homogeneous connections, and autonomous behavior decisions. For ordinary users, we construct an inequality-oriented behavior decision-making module, effectively addressing unequal social interactions and capturing the patterns of relationship adjustments driven by multi-dimensional factors. Experimental results demonstrate that DynamiX exhibits marked improvements in attitude evolution simulation and collective behavior analysis compared to static networks. Besides, DynamiX opens a new theoretical perspective on follower growth prediction, providing empirical evidence for opinion leaders cultivation.