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Related Experiment Video

Updated: Jun 26, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Enhancing swarms' durability to threats via graph signal processing and graph-neural-network-based generative

Jonathan Karin¹, Zoe Piran¹, Mor Nitzan²

¹Hebrew University of Jerusalem, School of Computer Science and Engineering, The , Jerusalem, Israel.

Physical Review. E

|January 21, 2026

Summary

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This study models swarms as graphs to analyze external threats, revealing a detectability-durability trade-off crucial for swarm stability. A new generative model, SwaGen, optimizes this trade-off for designing robust artificial swarms.

Area of Science:

Complex Systems
Graph Theory
Robotics

Background:

Swarms in nature and engineering exhibit complex dynamics.
Previous research overlooked external perturbations' impact on swarm stability.
Understanding swarm resilience to threats is critical for both natural and artificial systems.

Purpose of the Study:

To investigate the impact of external perturbations on swarm stability.
To uncover the relationship between swarm detectability and durability against threats.
To develop a generative model for designing resilient swarms.

Main Methods:

Modeling swarms as graphs and applying graph signal processing.
Analyzing predation as a perturbation signal on swarm graphs.

Related Experiment Videos

Last Updated: Jun 26, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Developing SwaGen, a graph neural network-based generative model for swarm optimization.

Main Results:

Identified a detectability-durability trade-off influencing swarm resilience.
Provided theoretical and empirical evidence linking this trade-off to spatial configuration.
SwaGen successfully generated unique spatial configurations optimizing the trade-off.

Conclusions:

External perturbations significantly affect swarm stability.
The detectability-durability trade-off is a key factor in swarm resilience.
SwaGen offers a novel approach for designing robust artificial swarms and understanding natural swarm dynamics.