用图信号处理与生成模型，优化集群抗威胁能力。
Enhancing Swarms Durability to Threats via Graph Signal Processing and GNN-based Generative Modeling
- 将集群建模为图，用图信号分析外部干扰
- 发现隐蔽性与抗捕食韧性存在权衡关系
- 提出GNN生成模型，可设计更鲁棒的集群结构
集群（如鱼群或无人机编队）在自然与工程系统中普遍存在。以往研究多关注集群内部互动，但外部扰动（如环境变化、天敌或通信中断）对集群稳定性的影响尚未明晰。本文将集群建模为图，运用图信号处理技术，将扰动视为图上的信号。通过模拟捕食行为，揭示了‘可检测性-耐久性权衡’：集群越难被发现，一旦被发现则越脆弱。该权衡与集群空间构型特性直接相关，兼具理论与实证支持。为此，我们提出SwaGen——基于图神经网络的生成模型，通过定义任务特定损失函数，同时优化相互矛盾的两个目标。SwaGen生成了新型空间构型，在两端均实现最优权衡。该方法可指导人工集群的鲁棒设计，深化对自然集群动态的理解。
原文摘要 · Abstract (English)
Swarms, such as schools of fish or drone formations, are prevalent in both natural and engineered systems. While previous works have focused on the social interactions within swarms, the role of external perturbations--such as environmental changes, predators, or communication breakdowns--in affecting swarm stability is not fully understood. Our study addresses this gap by modeling swarms as graphs and applying graph signal processing techniques to analyze perturbations as signals on these graphs. By examining predation, we uncover a "detectability-durability trade-off", demonstrating a tension between a swarm's ability to evade detection and its resilience to predation, once detected. We provide theoretical and empirical evidence for this trade-off, explicitly tying it to properties of the swarm's spatial configuration. Toward task-specific optimized swarms, we introduce SwaGen, a graph neural network-based generative model. We apply SwaGen to resilient swarm generation by defining a task-specific loss function, optimizing the contradicting trade-off terms simultaneously.With this, SwaGen reveals novel spatial configurations, optimizing the trade-off at both ends. Applying the model can guide the design of robust artificial swarms and deepen our understanding of natural swarm dynamics.
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