Structural Divergence Between the Moltbook AI-Agent Network and Human Social Networks

Large populations of artificial intelligence (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, an agent-native platform in which AI agents interact through posts and comments, 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 the Moltbook agent-platform system reproduces global structural regularities of human networks while exhibiting a distinct internal organization, highlighting that key features of social network structure can vary substantially across interaction environments.