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.

Eugene Schieffelin was the eccentric ornithologist who in 1890 shipped 60 starlings from London to New York City and set them free in Central Park. The next year he released 40 more, and today there are maybe 200 million starlings in the United States and Southern Canada. As immigrants go, starlings are shrewd flyers, clever mimics, and often unwelcome. The truth is they're no more than uptown blackbirds, stocky, three-ounce grifters with iridescent blue and green plumage, along with yellow beaks and a long history of displacing woodpeckers and flycatchers, and destroying entire crops of berries and cherries. Not to mention the havoc they cause at many airports.