Information Entropy Based on Propagation Feature of Node for Identifying the Influential Nodes

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic many other fields, is of great importance. By taking effect rate on information entropy into account, we proposed an improved (IIE) method. Compared benchmark methods six different empirical IIE method has been found with a better performance Kendall’s Tau imprecision function under Susceptible Infected Recovered (SIR) model. Especially Facebook network, grow by 120% as compared original IE And, there also equally good comparative analysis imprecise functions. The functions’ value smaller than networks.