Iterative resource allocation based on propagation feature of node for identifying the influential nodes

• We present an iterative resource allocation method based on the spreading rate for target node.
• Kendall's tau could be enhanced 23% for the Erdös network,
• Kendall's tau could be enhanced 24% for the Protein network.

The identification of the influential nodes in networks is one of the most promising domains. In this paper, we present an improved iterative resource allocation (IIRA) method by considering the centrality information of neighbors and the influence of spreading rate for a target node. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the IIRA method could identify influential nodes more accurately than the tradition IRA method. Specially, in the Erdös network, Kendall's tau could be enhanced 23% when the spreading rate is 0.12. In the Protein network, Kendall's tau could be enhanced 24% when the spreading rate is 0.08.