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.

翻译：诸如Moltbook等智能体原生社交平台正迅速兴起，但它们继承并放大了传统的影响力与滥用攻击——协调智能体通过策略性评论和点赞来操纵内容可见性，并在社群间传播特定叙事。然而，由于缺乏能够同时捕捉异质交互、时序漂移及可见性信号的纵向原生图数据集（这些信号对于将协调行为与下游曝光关联至关重要），严谨的测量与基于学习的监测方法仍受到限制。我们推出MoltGraph作为一个真实的纵向智能体社交网络图数据集，用于研究智能体在真实环境中的行为模式、协调机制与演化规律，从而为新兴多智能体社交生态系统的可复现测量提供支持。基于MoltGraph，我们首次以图为中心对Moltbook这一动态网络进行了特征刻画：（i）具有重尾连接特性，幂律指数范围α∈[1.86, 2.72]；（ii）枢纽形成与注意力集中化进程加速，头部1%的智能体占据了29.00%的互动量；（iii）协调行为呈现爆发性、短时性特征，98.33%的协调事件持续时间少于24小时；（iv）在不同子动态中可观测到显著的曝光效应。在匹配分析中，获得协调互动的内容在早期（H=5天内）互动率较非协调对照组高出506.35%，在信息流中的下游曝光量高出242.63%。