  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 Erdos network, the Kendall's tau
could be enhanced 23\% when the spreading rate is 0.12. In the Protein network,
the Kendall's tau could be enhanced 24\% when the spreading rate is 0.08.
