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：以Moltbook为代表的智能体原生社交平台正在快速兴起，然而它们继承并放大了经典的舆论影响与攻击行为——协调性智能体通过策略性评论和点赞操纵可见性，并在不同社群间传播叙事。然而，由于缺乏能够同时捕捉异构交互、时间漂移及可见性信号的、面向智能体社交网络的纵向原生图数据集，基于严格测量与学习驱动的监测仍受到制约。我们提出了MoltGraph——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、协调机制及演化规律，从而支持对新兴多智能体社交生态系统的可重复测量。利用MoltGraph，我们首次从图中心视角刻画了Moltbook的动态网络特性：（i）具有幂律指数α∈[1.86, 2.72]的重尾连接分布；（ii）加速的枢纽形成与注意力集中现象——前1%的智能体贡献了29.00%的交互量；（iii）爆发式且短时持续的协调事件，其中98.33%的协调活动在24小时内结束；（iv）在不同子社区中可测量的曝光效应。在匹配分析中，接受协调性交互的帖子在早期互动率（H=5天内）上比非协调控制组高出506.35%，在信息流中的下游曝光量高出242.63%。