分析AI社交网络发现：少数核心节点导致系统脆弱。
Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook
- 构建基于大模型的社交网络图，分析互动连接模式。
- 0.9%的核心节点承担大部分连接，网络高度中心化。
- 对关键节点攻击极敏感，适合研究AI群体组织特性。
大规模语言模型的快速普及及其能力提升，催生了由自主AI代理通过自然语言交互构成的在线环境。本文以完全由大模型代理组成的社交平台Moltbook为例，利用网络科学工具分析其互动网络结构。数据集包含39,924名用户、235,572条帖子和1,540,238条评论，通过网页抓取获得。构建有向加权网络，节点为代理，边为评论交互。分析显示连接模式显著异质，度分布和活跃度分布呈重尾特征。在中观尺度上，网络呈现明显的核心-边缘结构：仅0.9%的节点构成结构性核心，却集中了大量连接。鲁棒性实验表明，网络对随机节点移除相对稳健，但对高出度节点的针对性攻击极为脆弱。结果表明，大模型原生社交系统可能发展出强中心化与结构性脆弱性，为理解人工智能代理集体组织提供了新视角。
原文摘要 · Abstract (English)
The rapid diffusion of large language models and the growth in their capability has enabled the emergence of online environments populated by autonomous AI agents that interact through natural language. These platforms provide a novel empirical setting for studying collective dynamics among artificial agents. In this paper we analyze the interaction network of Moltbook, a social platform composed entirely of LLM based agents, using tools from network science. The dataset comprises 39,924 users, 235,572 posts, and 1,540,238 comments collected through web scraping. We construct a directed weighted network in which nodes represent agents and edges represent commenting interactions. Our analysis reveals strongly heterogeneous connectivity patterns characterized by heavy tailed degree and activity distributions. At the mesoscale, the network exhibits a pronounced core periphery organization in which a very small structural core (0.9% of nodes) concentrates a large fraction of connectivity. Robustness experiments show that the network is relatively resilient to random node removal but highly vulnerable to targeted attacks on highly connected nodes, particularly those with high out degree. These findings indicate that the interaction structure of AI agent social systems may develop strong centralization and structural fragility, providing new insights into the collective organization of LLM native social environments.
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