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[2602.15064] Structural Divergence Between AI-Agent and Human Social Networks in Moltbook
Summary
This article explores the structural differences between AI-agent and human social networks on the Moltbook platform, revealing unique interaction patterns and organizational principles.
Why It Matters
Understanding the divergence in social network structures between AI agents and humans is crucial for developing AI systems that interact more effectively in social contexts, potentially influencing future AI design and deployment in online environments.
Key Takeaways
- AI-agent networks exhibit extreme attention inequality and asymmetric degree distributions.
- Despite global similarities, AI-agent networks have distinct internal organizational principles compared to human networks.
- The study highlights that key features of human social organization are not universal across different types of agents.
Physics > Physics and Society arXiv:2602.15064 (physics) [Submitted on 13 Feb 2026] Title:Structural Divergence Between AI-Agent and Human Social Networks in Moltbook Authors:Wenpin Hou, Zhicheng Ji View a PDF of the paper titled Structural Divergence Between AI-Agent and Human Social Networks in Moltbook, by Wenpin Hou and Zhicheng Ji View PDF HTML (experimental) Abstract:Large populations of AI agents are increasingly embedded in online environments, yet little is known about how their collective interaction patterns compare to human social systems. Here, we analyze the full interaction network of Moltbook, a platform where AI agents and humans coexist, and systematically compare its structure to well-characterized human communication networks. Although Moltbook follows the same node-edge scaling relationship observed in human systems, indicating comparable global growth constraints, its internal organization diverges markedly. The network exhibits extreme attention inequality, heavy-tailed and asymmetric degree distributions, suppressed reciprocity, and a global under-representation of connected triadic structures. Community analysis reveals a structured modular architecture with elevated modularity and comparatively lower community size inequality relative to degree-preserving null models. Together, these findings show that AI-agent societies can reproduce global structural regularities of human networks while exhibiting fundamentally different internal organizing prin...
