Influential nodes identification using network local structural properties

Bin Wang,Jinfang Sheng,Junkai Zhang,Jinying Dai

doi:10.1038/s41598-022-05564-6

Abstract

With the rapid development of information technology, the scale of complex networks is increasing, which makes the spread of diseases and rumors harder to control. Identifying the influential nodes effectively and accurately is critical to predict and control the network system pertinently. Some existing influential nodes detection algorithms do not consider the impact of edges, resulting in the algorithm effect deviating from the expected. Some consider the global structure of the network, resulting in high computational complexity. To solve the above problems, based on the information entropy theory, we propose an influential nodes evaluation algorithm based on the entropy and the weight distribution of the edges connecting it to calculate the difference of edge weights and the influence of edge weights on neighbor nodes. We select eight real-world networks to verify the effectiveness and accuracy of the algorithm. We verify the infection size of each node and top-10 nodes according to the ranking results by the SIR model. Otherwise, the Kendall tau coefficient is used to examine the consistency of our algorithm with the SIR model. Based on the above experiments, the performance of the LENC algorithm is verified.

Highlights

With the rapid development of information technology, the scale of complex networks is increasing, which makes the spread of diseases and rumors harder to control
With the development of graph theory, the complex network has been applied in many fields[1,2,3,4]
To design a more applicable centrality measure, Xu et al.[19] proposed two influential nodes identification algorithms based on node adjacency information entropy (AIE)

Summary

Introduction

With the rapid development of information technology, the scale of complex networks is increasing, which makes the spread of diseases and rumors harder to control. The entropy of the node based on the weight distribution of the edges connected to it can be used as an indicator to evaluate the local influence of nodes. The local influence attributes of the neighbor nodes are added to obtain the entropy weight of the first and second-order edges of the nodes, which can be an indicator of the influence ability of the nodes in the network.

Results

Conclusion