  Cascading failures represent a fundamental threat to the integrity of complex
systems, often precipitating a comprehensive collapse across diverse
infrastructures and financial networks. This research articulates a robust and
pragmatic approach designed to attenuate the risk of such failures within
complex networks, emphasizing the pivotal role of local network topology. The
core of our strategy is an innovative algorithm that systematically identifies
a subset of critical nodes within the network, a subset whose relative size is
substantial in the context of the network's entirety. Enhancing this algorithm,
we employ a graph coloring heuristic to precisely isolate nodes of paramount
importance, thereby minimizing the subset size while maximizing strategic
value. Securing these nodes significantly bolsters network resilience against
cascading failures. The method proposed to identify critical nodes and
experimental results show that the proposed technique outperforms other typical
techniques in identifying critical nodes. We substantiate the superiority of
our approach through comparative analyses with existing mitigation strategies
and evaluate its performance across various network configurations and failure
scenarios. Empirical validation is provided via the application of our method
to real-world networks, confirming its potential as a strategic tool in
enhancing network robustness.
