A Fusion Method for Node Importance Measurement in Complex Networks

For the problem that closeness is difficult to effectively distinguish the importance of some nodes in complex networks, a new method of node importance measurement is proposed, which fuse the degree and closeness based on node re-ranking in segmentation. According to the network propagation dynamics model and Kendalls Tau coefficient, accuracy indicator and ranking stability indicator for evaluating measurement methods are given. Using the proposed method, simulations are carried out on Barabasi-Albert(BA) scale-free networks and ER random networks with different structures. The results show that compared with degree and closeness, fusion method not only has better measurement accuracy, but also has higher ranking stability.