The existing evaluation methods for node importance in complex network mainly focus on un-directed and un-weighed networks, and can't describe the real-world network completely. Most centrality measures only consider a single indicator, ignoring the difference between out-degree and in-degree of the node, and neglecting the importance of weight. Based on the directed-weighted complex network, this paper proposed a center node recognition algorithm, cw-shell decomposition method, which was based on out-degree, in-degree and weight, considering the difference between out-degree and in-degree of the node, and the practical importance of weight in real network. In order to verify the effectiveness of the new index, it used the weighted-susceptible-infectious-recovered model to simulate spreading process on real-world networks. The results show that the cw-shell decomposition method can rank the nodes efficiently, and identify the nodes with higher diffusion ability.

[2019-01-05] Printed Article /doc/2017.06.0648/v2