An Efficient Spreading Strategy Considering Information Decays and Partial Interactions Between People in Scale-Free Networks

Shuangyan Wang,Gang Mei

doi:10.1109/access.2019.2929556

Abstract

Complex networks are extensively applied to study the dynamics of real systems, such as how information spreads in social networks. Most existing spreading strategies are studied based on simple information spreading model, such as the optimized compartmental models in epidemics. However, for information spreading in social networks, more complex social factors should be considered. In this paper, we build an information spreading model that involves information decays and partial interactions based on multi-agent modeling in scale-free networks. The positive effects of a set of vertices on the spreading efficiency are discovered based on the proposed information spreading model. On the basis of the positive effects of those vertices, we propose an efficient spreading strategy for extending the spreading in scale-free networks. Ten groups of Monte Carlo experiments are conducted to verify the effectiveness of the proposed strategy. The experimental results demonstrated the effectiveness and validity of the proposed strategy. The proposed strategy can be exploited to extend the spread of warnings or control the spread of rumors in social networks.

Highlights

Complex networks are generally applied to analyze and study real complex systems [1], such as computer networks [2], [3], biological systems [4]–[6], the Internet of Things [7]–[9], human interactions [10]–[12], social networks [13]–[15], and epidemic dissemination [16], [17]
It can be observed that the numbers of informed vertices when using the proposed strategy are significantly larger than those when using the benchmark strategy in the five real scale-free networks
The Mean, Minimum, and Maximum of the 1000 numbers of informed vertices show that the metrics are always larger when using the proposed strategy than those when using the benchmark strategy in the five real scale-free networks

Summary

Introduction

Complex networks are generally applied to analyze and study real complex systems [1], such as computer networks [2], [3], biological systems [4]–[6], the Internet of Things [7]–[9], human interactions [10]–[12], social networks [13]–[15], and epidemic dissemination [16], [17] Information spreading, such as rumors, public opinions, and warnings, can be studied by transforming the real systems into graphs that are composed of vertices and edges. Wang et al [21] demonstrated that the spreading is more efficient when the large-degree vertices are specified as the initial spreaders. Zhou et al [26] deemed that the preference for

Results

Discussion

Conclusion