Abstract
Real-world systems, ranging from social to infrastructural, can be abstracted into complex networks. Promoting the spreading of some typical information (for instance, the commercial message, vaccination guidance, innovation, and political movement) on these networked systems can bring benefits to all aspects of society. In this study, we propose an effective edge-based approach for promoting the spreading of information on complex networks. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the information spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way to promote the spreading of information by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading dynamics.
Highlights
The subject of promoting the information spreading in networked systems is attracting substantial attention from multiple disciplines, for instance, computer science, statistical physics, and network science [1], [2]
We test the agreement between our susceptible– informed–recovered (SIR)–ee numerical approach proposed in Sec
We proposed an effective edge–based strategy for promoting the information spreading dynamics on complex networks
Summary
The subject of promoting the information spreading in networked systems is attracting substantial attention from multiple disciplines, for instance, computer science, statistical physics, and network science [1], [2]. Understanding the evolutionary mechanisms of the information spreading dynamics in real life and building suitable models to describe them play essential roles in developing promoting strategies. In some simple contagions (e.g., news diffusion and innovation spreading) where the informed individuals could transmit the information to those susceptible ones by a single contact, the classic susceptible– informed–susceptible (SIS) model [8], [9], susceptible– informed–recovered (SIR) model [10], [11] and many of their extensions [12]–[16] have been widely applied. For some complex contagions (e.g., behavior adoption [17] and political information spreading [18], [19]), researchers have proposed the threshold model which incorporates the social reinforcement mechanism (i.e., the mechanism that the susceptible individuals receive the information with a probability that increases with the cumulative number of contacts with the informed ones) [20], [21]. More spreading models with other complex mechanisms can be found in [22]–[24]
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