An Optimal Algorithm in Scale-Free Networks

In this paper, we propose an algorithm that can be efficiently used to search through scale-free networks. The algorithm uses local information such as the identities and connectedness of a node's neighbours, and its neighbours, but not the target's global position. We demonstrate that our search algorithm work well on a simulative networks, scale with the number of nodes, and may help reduce the network search traffic that tends to cripple such networks. We have studied how optimize nodes on a scale-free network using an association rules mining based on a novel Genetic Algorithm, we have proposed an designed specifically for discovering association rules. We compare the results of the Algorithm with the results of Apriori algorithm, and, it is better than it through the theoretic analysis and the experimental results. It can improve networks' robustness.