Using mapping entropy to identify node centrality in complex networks

•Mapping entropy is proposed based on the knowledge of a node and its neighbors.•Mapping entropy centrality is more efficient than the traditional centralities.•Mapping entropy centrality identifies the node importance well in complex network.•Dynamic attack using mapping entropy centrality is more efficient than static attack.

The problem of finding the best strategy to attack a network or immunize a population with a minimal number of nodes has attracted much current research interest. The assessment of node importance has been a fundamental issue in the research of complex networks. In this paper, we propose a new concept called mapping entropy (ME) to identify the importance of a node in the complex network. The concept is established according to the local information which considers the correlation among all neighbors of a node. We evaluate the efficiency of the centrality by static and dynamic attacks on standard network models and real-world networks. The simulation result shows that the new centrality is more efficient than traditional attack strategies, whether it is static or dynamic.