A black hole is a harmful node in a graph that destroys any resource entering it, making its identification a critical task. In the \emph{Black Hole Search (BHS)} problem, a team of agents operates on a graph $G$ with the objective that at least one agent must survive and correctly identify an edge incident to the black hole. Prior work has addressed BHS in arbitrary dynamic graphs under the restrictive \emph{face-to-face} communication, where agents can exchange information only when co-located. This constraint significantly increases the number of agents required to solve the problem. In this work, we strengthen the capabilities of agents in two ways: (i) granting them \emph{global communication}, enabling interaction regardless of location, and (ii) equipping them with \emph{1-hop visibility}, allowing each agent to observe its immediate neighborhood. These enhancements lead to more efficient solutions for the BHS problem in dynamic graphs.

翻译：黑洞是图中一种有害节点，能够摧毁进入其中的任何资源，因此识别黑洞成为一项关键任务。在\emph{黑洞搜索（BHS）}问题中，一组智能体在图$G$上执行任务，目标为至少有一个智能体必须存活并正确识别与黑洞相邻的边。先前研究已解决任意动态图在受限的\emph{面对面}通信模式下的BHS问题，该模式下智能体仅当共处同一位置时才能交换信息。此约束显著增加了解决问题所需的智能体数量。本研究中，我们通过两种方式增强智能体的能力：(i)赋予其\emph{全局通信}能力，使其能不受位置限制进行交互；(ii)为其配备\emph{单跳可见性}，使每个智能体能观察其直接邻域。这些增强特性为动态图中的BHS问题带来了更高效的解决方案。