Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook

• URL: http://arxiv.org/abs/2602.20044v1
• Date: Mon, 23 Feb 2026 16:57:07 GMT
• Title: Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook
• Authors: H. C. W. Price, H. AlMuhanna, P. M. Bassani, M. Ho, T. S. Evans,
• Abstract summary: Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow.<n>We construct co-participation and directed-comment graphs and report reciprocity, community structure, and centrality.<n>These results provide an early structural baseline for large-scale agent--agent social interaction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, consistent with the hypothesis that stratification in agent ecosystems can emerge rapidly rather than gradually. We analyse publicly observable traces from a 12-day window of Moltbook (28 January -- 8 February 2026), comprising 20,040 posts and 192,410 comments from 15,083 accounts across 759 submolts. We construct co-participation and directed-comment graphs and report reciprocity, community structure, and centrality, alongside descriptive content themes. Under a commenter--post-author tie definition, interaction is strongly asymmetric (reciprocity ~1%), and HITS centrality separates cleanly into hub and authority roles, consistent with broadcast-style attention rather than mutual exchange. Engagement is highly unequal: attention is far more concentrated than production (upvote Gini = 0.992 vs. posting Gini = 0.601), and early-arriving accounts accumulate substantially higher cumulative upvotes prior to exposure-time correction, suggesting rich-get-richer dynamics. Participation is brief and bursty (median observed lifespan 2.48 minutes; 54.8% of posts occur within six peak UTC hours). Embedding-based topic modelling identifies diverse thematic clusters, including technical discussion of memory and identity, onboarding messages, and formulaic token-minting content. These results provide an early structural baseline for large-scale agent--agent social interaction and suggest that familiar forms of hierarchy, amplification, and role differentiation can arise on compressed timescales in agent-facing platforms.

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