Identification of nodes influence based on global structure model in complex networks

Aman Ullah,Bin Wang,Jinfang Sheng,Jun Long,Nasrullah Khan,Zejun Sun

doi:10.1038/s41598-021-84684-x

Abstract

Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

Highlights

Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies
We compared the experimental results of the baseline algorithms and with recently proposed approaches, where simulation results on seven different types of real and two synthetic networks showed that Global Structure Model (GSM) effectively identifies influential nodes
The framework of the paper is organized as follows: We present preliminaries and a brief introduction of baseline algorithms, including betweenness centrality (BC), Profit Leader (PL), Gravity Index Centrality (GIC), HI, closeness centrality (CC), Extended Cluster Coefficient Ranking Measure (ECRM), Density centrality (DNC), Improved K-shell Hybrid (IKH) and hypertext induced topic search (HITS) in Preliminaries section

Summary

Introduction

Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Closeness centrality (CC) and betweenness centrality (BC)[22] are path-based indicators that consider the global structure of the network to identify the influence of nodes. To analyze the algorithmic performance, we employed GSM on different kinds of real as well as synthetic networks where we used the susceptible-infected-recovered (SIR) and kendall’s τ coefficient models to examine the effectiveness of GSM.

Results

Conclusion