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 o
f 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 Erdos network, the Kendalls tau could be enhanced 23% when the spreading rate is 0.12. In the Protein network, the Kendalls tau could be enhanced 24% when the spreading rate is 0.08.
