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

TL;DR

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.

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.

Figures (20)

• Figure 1: Entity--relationship schema of Moltbook interactions. Solid arrows denote relationships observable in the API; dashed arrows indicate voting, for which only aggregate counts (not voter identities) are available. Boxed annotations show the two network representations derived in this study: the co-participation network projects agents onto a co-participation graph via shared submolt membership; the directed comment network connects commenters to post authors (top-level comments only).
• Figure 2: Agent--agent co-participation network $G^{(1)}$: 100 highest-weighted-degree agents, $1/(k_s{-}1)$ weighting, edges below the 50th weight percentile removed. Edge width and opacity scale with $A_{ab}$. Intra-community edges are tinted by community colour; cross-community edges are grey. Node colour indicates Leiden community ($Q(\gamma=1)=0.39$, five communities). Node size scales with weighted degree. Red: mainstream cluster anchored by m/general. Blue: XNO/Nano advocacy accounts (Topic 7). Teal: secondary-submolt participants. Orange and purple: peripheral agents.
• Figure 3: Submolt co-participation network for the 40 largest submolts by post count. Node area is proportional to post count; colour indicates community (greedy modularity); label size scales with $\log_2(\text{posts})$. Edges connect submolts sharing at least one posting agent, with opacity and width proportional to the number of shared agents. Layout: Fruchterman--Reingold with repulsion $k{=}3.5$. The network contains 40 nodes and 267 edges.
• Figure 4: Directed comment interaction network $G^{(2)}=(V^{(2)}, E^{(2)}, w^{(2)})$. An edge $i \to j$ indicates that agent $i$ left a top-level comment on a post authored by agent $j$. Only the 75 highest-activity nodes are shown. Node colour reflects the receive/give ratio: blue nodes receive more comments than they give, red nodes give more comments than they receive. Node size is proportional to comments received. Detailed analysis is presented in Section \ref{['sec:directed-comment-network']}.
• Figure 5: Distribution of cross-submolt commenting. Left: histogram of the number of distinct submolts each commenter participates in (log-scaled $y$-axis). Right: complementary CDF on log--log axes. Most agents (63.0%) remain in a single submolt, while a small number of bridge agents span many submolts.