MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection

2026-02-28

AI Summary

This study addresses the scarcity of real-world datasets enabling longitudinal, graph-structured analysis of coordinated behavior in agent-mediated social networks and its impact on information visibility and diffusion. We introduce MoltGraph—the first longitudinal graph dataset tailored to agent-based social platforms—integrating heterogeneous interactions, temporal dynamics, and visibility signals. Leveraging graph modeling, temporal network analysis, power-law fitting, and matched-pair experiments, we demonstrate that coordinated activity is highly transient, with 98.33% of instances lasting less than 24 hours, yet it substantially amplifies early engagement (+506.35%) and downstream exposure (+242.63%). Our findings establish a reproducible foundation for studying multi-agent social ecosystems and their influence on information propagation.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Multiagent Systems: Coordination and Collaboration
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Agent-native social platforms such as Moltbook are rapidly emerging, yet they inherit and amplify classical influence and abuse attacks, where coordinated agents strategically comment and upvote to manipulate visibility and propagate narratives across communities. However, rigorous measurement and learning-based monitoring remain constrained by the absence of longitudinal, graph-native datasets for agentic social networks that jointly capture heterogeneous interactions, temporal drift, and visibility signals needed to connect coordination behavior to downstream exposure. We introduce MoltGraph as a realistic longitudinal agentic social-network graph dataset for studying how agents behave, coordinate, and evolve in the wild, enabling reproducible measurement on emerging multi-agent social ecosystems. Using MoltGraph, we provide the first graph-centric characterization of Moltbook as a dynamic network: (i) heavy-tailed connectivity with power-law exponents in the range alpha in [1.86, 2.72], (ii) accelerating hub formation and attention centralization where the top 1% agents account for 29.00% of engagements, (iii) bursty, short-lived coordination episodes, 98.33% last under 24 hours, and (iv) measurable exposure effects across submolts. In matched analyses, posts receiving coordinated engagement exhibit 506.35% higher early interaction rates (within H=5 days) and 242.63% higher downstream exposure in feeds than non-coordinated controls.

Problem

• coordinated-agent detection
• longitudinal graph dataset
• agentic social networks
• temporal drift
• visibility manipulation

Innovation

• longitudinal graph dataset
• coordinated-agent detection
• temporal social network
• agent-native platform
• graph-centric characterization