A diameter path based method for important node detection in complex network

The strategies for important node detection according to topological structures are widely explored in complex networks. The diameter is a very important topological parameter among various network topological indicators. However, it is seldom utilized in important node searching methods. In this study, we defined the nodes on the diameter paths as central nodes and proposed a Diameter Center Detection (DCD) method to search the central nodes. In the experiments, the DCD method is applied to three deterministic networks, a series of small-world networks, scale-free networks and five real networks, respectively. The experimental results show that the central nodes searched by DCD have advantages over the nodes of the whole network in the evaluations of various centrality measures, e.g. Betweenness Centrality (BC), Closeness Centrality (CC), Degree Centrality (DC) and k-shell decomposition results. In addition, after deleting central nodes, the network structure changes a lot on the perspective from both diameter and giant component. Furthermore, the experimental results show that the edge deleting policy based on DCD is effective in the way that deleting fewer edges disrupting more node pair connectivity.