The spread of infectious diseases, rumors, and harmful speech in networks can result in substantial losses, underscoring the significance of studying how to suppress such hazardous events. However, previous studies often assume full knowledge of the network structure, which is often not the case in real-world scenarios. In this paper, we address the challenge of controlling the propagation of hazardous events by removing nodes when the network structure is unknown. To tackle this problem, we propose a hierarchical reinforcement learning method that drastically reduces the action space, making the problem feasible to solve. Simulation experiments demonstrate the superiority of our method over the baseline methods. Remarkably, even though the baseline methods possess extensive knowledge of the network structure, while our method has no prior information about it, our approach still achieves better results.

翻译：传染病、谣言及有害言论在网络中的传播可能造成重大损失，因此研究如何抑制此类危害事件具有重要意义。然而，以往的研究通常假设完全掌握网络结构信息，但这在现实场景中往往难以实现。本文针对网络结构未知情况下通过删除节点来控制危害事件传播的挑战性问题展开研究。为解决该问题，我们提出了一种层次强化学习方法，该方法能大幅缩减动作空间，从而使问题具备可解性。仿真实验表明，该方法优于基线方法。值得注意的是，尽管基线方法对网络结构有充分了解，而我们的方法在无先验网络结构信息的条件下，仍能获得更优效果。