[Purpose/significance] For the identification of important nodes in complex networks, the paper designs a node centrality algorithm, which plays an important role in infectious disease prevention and control, public opinion monitoring, product marketing, talent discovery and so on. [Method/process] This paper proposed a new node centrality algorithm, the HHa node centrality algorithm, taking both the number of the node's high influence neighbors and their total influence into consideration. On the real network and artificial network, the Susceptible-Infected-Recovered (SIR) model was used to simulate the information dissemination process, and the monotonic function M and Kendall correlation coefficient were used as evaluation indicators to verify the effectiveness, accuracy and stability of the HHa centrality algorithm. [Result/conclusion] The experimental results show that, compared with the 7 classic centrality algorithms, the HHa centrality algorithm ranks 2nd with a monotonic result of 0.999, and the Kendall coefficient is 0.845, which is higher than other algorithms' accuracy about 0.15, ranking 1st and performing robustly. It is feasible to use HHa centrality algorithm to identify important nodes in the network.
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