Enhancing Swarms Durability to Threats via Graph Signal Processing and GNN-based Generative Modeling

2025-07-03

AI Summary

Collective intelligent systems (e.g., fish schools, UAV swarms) suffer from insufficient stability under external threats—such as predators, environmental disturbances, or communication failures—due to a fundamental trade-off between detectability and durability: reduced detectability often compromises post-attack survivability. Method: We propose a graph signal processing–based framework to model perturbation propagation, enabling quantitative characterization of structural detectability and attack resilience. Building upon this, we design SwaGen, a task-driven generative graph neural network that jointly optimizes swarm topology for robustness against dynamic disturbances. Contribution/Results: Theoretical analysis and extensive simulations demonstrate that SwaGen generates novel spatial configurations that significantly enhance collective robustness and survival rates under time-varying threats. This work establishes a new paradigm for understanding natural collective behaviors and designing resilient artificial swarm systems.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Analyzing swarm stability under external perturbations using graph signal processing
• Investigating detectability-durability trade-off in swarms during predation scenarios
• Developing GNN-based SwaGen model for resilient swarm generation and optimization

Innovation

• Modeling swarms as graphs for signal processing
• Introducing SwaGen for generative swarm optimization
• Optimizing detectability-durability trade-off via GNN