Abstract
In recent years, information spreading mechanism on the social networks has received extensive attention. Previous studies usually simplify the social relationships as binary, though in real social networks, the different intimacy between nodes in different layers will affect the information spreading. The enhancement of non-redundant information memory also plays an important role in information spreading. In this paper, we propose a weighted two-layered social network information spreading model based on threshold model. In order to qualitatively understand the impact of weight distribution heterogeneity on information spreading, an edge-weight based compartmental theory is proposed. We find that under an arbitrary adoption threshold, reducing weight distribution heterogeneity can facilitate information spreading and promote the global adoption, yet unable to change the dependence pattern of the final adoption size on the unit transmission probability. We also find that when the initial seed fraction is less than a critical value, information cannot be transmitted explosively in the network. Increasing the fraction of initial seeds or the heterogeneity of degree distribution will alter the dependence pattern of the final adoption size on the unit transmission probability from being discontinuous to being continuous.
Highlights
With the rapid development of Internet, social networks are becoming ever more pervasive in people’s lives
According to the experimental results, we find that reducing the heterogeneity of weight distribution can accelerate information spreading, but it can not change the growth pattern of the final adoption size
SIMULATION RESULTS AND ANALYSIS In reality, users may have different social relationships on different networks, so we need to consider the heterogeneity of degree distribution between different networks
Summary
With the rapid development of Internet, social networks are becoming ever more pervasive in people’s lives. Instant messengers [1], Weibo [2], Facebook [3] and other social softwares provide powerful means of information sharing [4], which take up people’s fragmented time, and provide great convenience Such expansion and facilitation have speeded up information spreading [5], and changed the fundamental mechanisms underlying the emerging complex spreading patterns. Classical models such as the independent cascade model (ICM) and the threshold model [16] have attracted more and more attentions Based on these models, researchers have identified many potential factors that can affect information contagion mechanism, including the source node [17], community structure [18], asymptomatic infection [19], coreness [20], and so on
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