This thesis investigates signal propagation and stability in spider web-like networks, focusing on how velocity differences, structural geometry, and complexity influence network behavior. Spider webs, known for their resilience, flexibility, and efficient vibration transmission, offer valuable insights into designing robust artificial networks. By employing mathematical and physical modeling, this study explores force distribution, signal propagation dynamics, and collision phenomena within these networks.

The study introduces distinct propagation approaches, ranging from simple discrete collision analysis to advanced continuous simulations incorporating energy dissipation, adaptive weighting, and refined collision detection algorithms. Key methodologies include simulations of force distribution using recurrence relations, random walk models, and wavefront propagation models to examine how signals traverse complex network topologies. These simulations reveal that network topology significantly impacts signal efficiency, propagation speed, collision frequency, and signal loss, with central nodes emerging as critical hubs of activity and congestion. Additionally, structural defects such as inactive nodes, altered masses, and weakened edges are systematically introduced to evaluate their influence on the overall stability and signal propagation efficiency. These imperfections profoundly affect network performance, demonstrating the necessity for structural adaptability and redundancy to maintain integrity under stress.