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.