Abstract
The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási–Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (i) the proposed selection strategy can significantly improve spreading efficiency in the short-term spreading and (ii) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.
Highlights
In the analysis of social networks, a typical problem is how to effectively diffuse information by exploiting different spreading strategies and/or different selection methods in varied networks
The results indicate that for short-term spreading with limited time, the SIIVs strategy is capable of significantly improving spreading efficiency in networks with 500 people
The results indicate that for the short-term spreading with limited time, the SIIVs strategy is capable of significantly improving spreading efficiency in networks with 2000 people
Summary
In the analysis of social networks, a typical problem is how to effectively diffuse information by exploiting different spreading strategies and/or different selection methods in varied networks. Yang et al [25,26] revealed that the vertices with the larger k-shell were more influential spreaders in the spreading complex network; and they improved and proposed the relevant measuring methods based on the traditional K-Shell decomposition. Lei Gao et al [27] indicated that the information can be diffused efficiently by selecting and preferentially spreading to small-degree neighbours with small informed density. For information spreading in an emergency, selecting influential neighbours based on the vertex degree is an effective approach for maximally diffusing the information
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