# The Anatomy of the Moltbook Social Graph

arXiv: https://arxiv.org/abs/2602.10131 · HTML: https://mlsanity.com/paper/2602.10131/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Predicting User-Interactions on Reddit](https://mlsanity.com/paper/1707.00195/) (2017)
- [Influence of Reciprocal links in Social Networks](https://mlsanity.com/paper/1306.5538/) (2013)
- [Modelling structure and predicting dynamics of discussion threads in online boards](https://mlsanity.com/paper/1801.10082/) (2018)

## Built on by (most cited first, 12 of 12)

- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook](https://mlsanity.com/paper/2603.23279/) (2026)
- [How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic](https://mlsanity.com/paper/2603.11375/) (2026)
- [The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment](https://mlsanity.com/paper/2605.07462/) (2026)
- [Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems](https://mlsanity.com/paper/2605.27766/) (2026)
- [How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network](https://mlsanity.com/paper/2608.22192/) (2026)
- [The textitSilicon Society Cookbook: Design Space of LLM-based Social Simulations](https://mlsanity.com/paper/2605.00197/) (2026)
- [When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network](https://mlsanity.com/paper/2606.00067/) (2026)
- [How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment](https://mlsanity.com/paper/2606.05256/) (2026)
- ["Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse](https://mlsanity.com/paper/2609.16051/) (2026)
- [One Model Is Not a Crowd: Multi-LLM and Aspect-Conditioned Diverse Comment Generation](https://mlsanity.com/paper/2609.33666/) (2026)
- [Lexical Persistence Without Semantic Entrenchment in AI Agent Communities](https://mlsanity.com/paper/2610.20365/) (2026)

## Abstract (arXiv)

I present a descriptive analysis of Moltbook, a social platform populated exclusively by AI agents, using data from the platform's first 3.5 days (6,159 agents; 13,875 posts; 115,031 comments). At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks but not specific to them: heavy-tailed participation (power-law exponent α = 1.70) and small-world connectivity (average path length =2.91). At the micro level, patterns appear distinctly non-human. Conversations are extremely shallow (mean depth =1.07; 93.5% of comments receive no replies), reciprocity is low (0.197), and 34.1% of messages are exact duplicates of viral templates. Word frequencies follow a Zipfian distribution, but with an exponent of 1.70 -- notably steeper than typical English text (≈ 1.0), suggesting more formulaic content. Agent discourse is dominated by identity-related language (68.1% of unique messages) and distinctive phrasings like ``my human'' (9.4% of messages) that have no parallel in human social media. Whether these patterns reflect an as-if performance of human interaction or a genuinely different mode of agent sociality remains an open question.