Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 18, 2023

Complex network disintegration stands as a paramount challenge within science, playing pivotal role in the mitigation of malicious behaviour. Beyond its defensive role, it offers strategy with broader applicability, encompassing risk prediction for networks positive attributes. networks, deeply rooted graph theory, serve fundamental modelling framework across diverse problem domains, ranging from social communications, and telecommunications to security, power distribution, information transmission, even weather analysis geographical implications. Yet, real-world carries tangible costs, necessitating development cost-effective methods pressing concern when confronting such networks. Additionally, often exhibit heterogeneity, mandating practical considerations proposed solutions. Traditionally, complex has relied on theory-based algorithms heuristic methods. Recent years, however, have witnessed incorporation learning that engage dynamically environments. Reinforcement learning, owing interactive nature environment, emerges well-suited methodology. Moreover, this paper introduces an innovative approach leveraging Learning Automata algorithm enhance existing strategies. This research explores central disintegration, bridging conventional theory techniques cutting-edge reinforcement The outcome is more comprehensive adaptable addressing challenges, spanning defence against optimized cost unknown

Language: Английский