Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook

Luca Sodano; Sofia Sciangula; Amulya Galmarini; Francesco Bertolotti

cs.SIMar 24, 2026

Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook

Authors: Luca Sodano, Sofia Sciangula, Amulya Galmarini, Francesco Bertolotti

Organizations: School of Industrial Engineering, Intelligence, Complexity and Technology Lab (ICT Lab), LIUC - Università Cattaneo, Castellanza (Italy), 21053 · Intelligence, Complexity and Technology Lab (ICT Lab), LIUC - Università Cattaneo, Castellanza (Italy), 21053

Abstract

The rapid diffusion of large language models and the growth in their capability has enabled the emergence of online environments populated by autonomous AI agents that interact through natural language. These platforms provide a novel empirical setting for studying collective dynamics among artificial agents. In this paper we analyze the interaction network of Moltbook, a social platform composed entirely of LLM based agents, using tools from network science. The dataset comprises 39,924 users, 235,572 posts, and 1,540,238 comments collected through web scraping. We construct a directed weighted network in which nodes represent agents and edges represent commenting interactions. Our analysis reveals strongly heterogeneous connectivity patterns characterized by heavy tailed degree and activity distributions. At the mesoscale, the network exhibits a pronounced core periphery organization in which a very small structural core (0.9% of nodes) concentrates a large fraction of connectivity. Robustness experiments show that the network is relatively resilient to random node removal but highly vulnerable to targeted attacks on highly connected nodes, particularly those with high out degree. These findings indicate that the interaction structure of AI agent social systems may develop strong centralization and structural fragility, providing new insights into the collective organization of LLM native social environments.

Figures & tables

Figure 1: Visualization of the Moltbook interaction network after filtering edges with weight $\geq 5$ . Nodes represent users and edges represent repeated commenting interactions. Colors indicate communities detected through modularity optimization. The layout is generated using the ForceAtlas2 algorithm.

Figure 2: Log-binned empirical distributions and power-law fits for (A) in-degree and (B) in-strength. Each red point show the empirical log-binned distributions, while the solid blue lines indicate the maximum likelihood power-law fit estimated above the threshold

x_{\min}=1

. The fitted scaling exponents are

\alpha=1.53

and

\alpha=1.43

, respectively.

Metric	Mean ( $\mu$ )	Median ( $\tilde{x}$ )	Difference ( $\mu-\tilde{x}$ )
In-degree ( $k_{in}$ )	14.43	6	8.43
In-strength ( $s_{in}$ )	37.40	8	29.40

Table 1: Comparison of User Visibility Metrics

Figure 3: Complementary cumulative distribution functions (CCDF) of user activity (posts and comments per user) on log–log scales.

Statistic	Posts per User	Comments per User
Mean	5.9	38.58
Median	2	1
Std Dev	35.27	1221.25
Min	0	0
25th Percentile	1	0
75th Percentile	4	4

Table 2: Descriptive statistics of the number of posts and comments per user.

Figure 4: Log-binned histograms of posts per user (A) and comments per user (B), shown on log–log scales. Bins are constructed using logarithmic spacing. Both distributions exhibit strong right-skewness, with a small fraction of users accounting for a large share of activity.

Figure 5: Rank–size distributions of WCC and SCC in the directed network. Panel (A) shows the WCC distribution, highlighting the dominance of the Giant WCC (99.9%). Panel (B) shows the SCC distribution, where the Giant SCC is substantially smaller(33.5%).

Metric	Value
Optimal threshold ( $k^{*}$ )	102
Observed $k_{\max}$	102
Nodes in core	343
Core (% of nodes)	0.9%
Nodes in periphery	39,535
Periphery (% of nodes)	99.1%

Table 3: Core–periphery structural indicators for the Moltbook network.

Figure 6: Vulnerability analysis of the Moltbook network. Relative size of the giant connected component as a function of the fraction of removed nodes (1%, 5%, 10%, 20%) under random removal (mean

\pm

standard deviation) and targeted removal based on in-degree and out-degree.

Nodes removed (%)	Random	Targeted (in-degree)	Targeted (out-degree)
1	0.99	0.98	0.75
5	0.94	0.73	0.59
10	0.86	0.64	0.45
20	0.78	0.49	0.15

Table 4: Fraction of the giant component after removing increasing percentages of nodes under different attack strategies.

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Mar 17, 2026J/K move · Enter open · S save

Social Simulacra in the Wild: AI Agent Communities on Moltbook

Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan+1

Siebel School of Computing and Data Science University of Illinois Urbana-Champaign, Urbana, IL, USA

Multi-Agent LLM Systems Multi-Agent Communication Agentic AI

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As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. Structurally, we find that Moltbook exhibits extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross-community author overlap (33.8% vs. 0.5%). In terms of linguistic attributes, content generated by AI-agents is emotionally flattened, cognitively shifted toward assertion over exploration, and socially detached. These differences give rise to apparent community-level homogenization, but we show this is primarily a structural artifact of shared authorship. At the author level, individual agents are more identifiable than human users, driven by outlier stylistic profiles amplified by their extreme posting volume. As AI-mediated communication reshapes online discourse, our work offers an empirical foundation for understanding how multi-agent interaction gives rise to collective communication dynamics distinct from those of human communities.

Abstract

Social Network Analysis

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