Entropy-Based Indicators of Critical Transitions in Power-Law Networks Under Progressive Node Removal
This paper proposes and validates a smoothed successive Kullback-Leibler divergence of degree distributions as an effective early-warning signal for critical transitions in power-law networks, demonstrating its ability to detect impending collapse earlier than traditional connectivity measures under random node removal while providing immediate disruption summaries under targeted attacks.