The authors study the resilience of scale-free networks to intentional attacks, where a fraction \( p \) of the most connected sites is removed. They use percolation theory to analyze and numerically simulate the critical fraction \( p_c \) needed for network disintegration and the size of the largest connected cluster. The study finds that even networks with \( \alpha \leq 3 \), known to be resilient to random site removal, are highly sensitive to intentional attacks. Near the critical threshold, the average distance between sites in the largest cluster scales as \( \sqrt{M} \) rather than \( \log_k M \), indicating that the disruptive effects of intentional attack become significant even before the critical threshold is reached. The results support the idea that scale-free networks are vulnerable to targeted attacks, particularly when the network is close to criticality.The authors study the resilience of scale-free networks to intentional attacks, where a fraction \( p \) of the most connected sites is removed. They use percolation theory to analyze and numerically simulate the critical fraction \( p_c \) needed for network disintegration and the size of the largest connected cluster. The study finds that even networks with \( \alpha \leq 3 \), known to be resilient to random site removal, are highly sensitive to intentional attacks. Near the critical threshold, the average distance between sites in the largest cluster scales as \( \sqrt{M} \) rather than \( \log_k M \), indicating that the disruptive effects of intentional attack become significant even before the critical threshold is reached. The results support the idea that scale-free networks are vulnerable to targeted attacks, particularly when the network is close to criticality.