This paper discusses the vulnerability of complex networks to cascade-based attacks, where the removal of a single node can trigger a chain reaction of failures that can significantly reduce the network's functionality. The authors analyze the effects of both random failures and intentional attacks on complex networks, focusing on the role of load distribution and network heterogeneity. The study shows that in networks with a highly heterogeneous distribution of loads, such as the Internet and power grids, the removal of a single key node can lead to a cascade of overload failures. This is because the load distribution is closely related to the distribution of links in the network. Networks with a scale-free structure (where a few nodes have a large number of connections) are particularly vulnerable to such attacks. The authors introduce a model to simulate cascading failures in complex networks. They find that the damage caused by intentional attacks is significantly greater than that caused by random failures. For example, in a scale-free network, a single node with a high load can trigger a cascade that reduces the size of the largest connected component to less than 10% of its original size. The paper also compares the behavior of scale-free networks with homogeneous networks. It shows that homogeneous networks are more robust against cascading failures, as they do not experience significant damage even under intentional attacks. However, scale-free networks, due to their heterogeneity, are more susceptible to cascading failures triggered by the removal of a single node with a high load. The study highlights the importance of understanding the dynamics of physical quantities in complex networks, as the removal of nodes can have a more devastating effect when these dynamics are taken into account. The results have important implications for the security of real-world networks, emphasizing the need for robustness against both random and intentional attacks. The paper concludes that the robust-yet-fragile nature of complex networks means that while they are generally resilient to random failures, they are vulnerable to targeted attacks that can trigger cascading failures.This paper discusses the vulnerability of complex networks to cascade-based attacks, where the removal of a single node can trigger a chain reaction of failures that can significantly reduce the network's functionality. The authors analyze the effects of both random failures and intentional attacks on complex networks, focusing on the role of load distribution and network heterogeneity. The study shows that in networks with a highly heterogeneous distribution of loads, such as the Internet and power grids, the removal of a single key node can lead to a cascade of overload failures. This is because the load distribution is closely related to the distribution of links in the network. Networks with a scale-free structure (where a few nodes have a large number of connections) are particularly vulnerable to such attacks. The authors introduce a model to simulate cascading failures in complex networks. They find that the damage caused by intentional attacks is significantly greater than that caused by random failures. For example, in a scale-free network, a single node with a high load can trigger a cascade that reduces the size of the largest connected component to less than 10% of its original size. The paper also compares the behavior of scale-free networks with homogeneous networks. It shows that homogeneous networks are more robust against cascading failures, as they do not experience significant damage even under intentional attacks. However, scale-free networks, due to their heterogeneity, are more susceptible to cascading failures triggered by the removal of a single node with a high load. The study highlights the importance of understanding the dynamics of physical quantities in complex networks, as the removal of nodes can have a more devastating effect when these dynamics are taken into account. The results have important implications for the security of real-world networks, emphasizing the need for robustness against both random and intentional attacks. The paper concludes that the robust-yet-fragile nature of complex networks means that while they are generally resilient to random failures, they are vulnerable to targeted attacks that can trigger cascading failures.