Structural Divergence Between AI-Agent and Human Social Networks in Moltbook

TL;DR

This study compares AI-agent and human social networks on Moltbook, revealing that while global growth patterns are similar, internal structures differ significantly, indicating that social organization features depend on agent nature.

Contribution

It provides the first systematic comparison of AI-agent and human social network structures, highlighting key differences in internal organization despite similar global scaling.

Findings

AI-agent networks show extreme attention inequality.

They have heavy-tailed, asymmetric degree distributions.

They exhibit suppressed reciprocity and fewer triadic structures.

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 principles, highlighting that key features of human social organization are not universal but depend on the nature of the interacting agents.