Evaluation of Node Importance in Complex Networks Based on Improved Node Contraction Method
Identifying the important nodes in the network has important practical value for studying the topology and functional characteristics of the network.In order to better mine the important nodes in complex networks,considering the impact of the changes of node core location and network topology on the evaluation of node importance,two improved evaluation methods of important nodes in complex networks are pro-posed based on the node shrinkage method,and simulation experiments and comparative analysis are carried out.On the hand,combined with the characteristic that k-shell value can evaluate the coarse-grained location of nodes,the ratio of k-shell value of nodes to the sum of all k-shell values in the network is taken as the coefficient of node importance(IMC)obtained by node shrinkage method,and an improved algo-rithm based on the new node importance K-IMC is proposed;On the other hand,the change of network topology is described by network struc-ture entropy.Combined with the change of network standard structure entropy before and after shrinkage,an improved algorithm based on an-other new node importance E-IMC is proposed.On this basis,simulation experiments are carried out on these two improved important node evaluation algorithms,and the performance of the algorithm is evaluated and analyzed by using SIR model and robustness test.The experimen-tal results show that K-IMC algorithm and E-IMC algorithm show better accuracy in sorting important nodes compared with the original node shrinking method.In terms of accuracy,E-IMC algorithm is higher than K-IMC algorithm,and in terms of operational efficiency,K-IMC al-gorithm is better than E-IMC algorithm.
万方数据
维普
