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

Linfeng Zhong,Chen Luo,Yu Bai,Yan Tian,Jin Huang,Weijun Pan,Chenquan Gan

doi:10.1155/2021/5554322

Abstract

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

Highlights

E identification of influential nodes is of great significance in fields of epidemic and rumor control, targeted advertising, and air traffic planning [35, 36]
Data Description. ere are six empirical networks used to evaluate the performance of the information entropy (IIE) method. e US air network [53] is an integral part of the US air traffic networks
By considering the information entropy and spreading rate of the target nodes, we proposed an improved information entropy (IIE) method. e IIE method takes the spreading rate and the number of the target node’s neighbors into account

Summary

Introduction

E identification of influential nodes is of great significance in fields of epidemic and rumor control, targeted advertising, and air traffic planning [35, 36]. Degree centrality can be regarded as a typical method to deal with the former problem in consideration for the local information [37, 38] In view of this idea, Chen et al proposed the Local Rank method by considering the 4th order neighbors of the node [39]. In the example network that is presented, the influence of nodes 1 and 6 cannot be accurately identified by the IE method In this case, we think that the neighbors’ number and spreading rate are likely to have a positive effect on the target node. We think that the neighbors’ number and spreading rate are likely to have a positive effect on the target node Based on this idea, we proposed an improved information entropy (IIE) method in which the target node’s information entropy may be affected by the propagation feature. Compared with the benchmark methods in six real networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under the Susceptible Infected Recovered (SIR) model [51, 52]

Methods

Results

Conclusion