In this paper, we study the information diffusion structure on social networks with general degree distribution. To describe the information diffusion structure, we adopt six different viewpoints of metrics, including structural virality, distance variance, distance variability, distance susceptibility, cascade depth and cascade width. On Erdös–Rényi (ER) networks, we can intuitively see that as the diffusion tree becomes denser, the depth of the diffusion tree first increases to a peak and then decreases with the infection rate increasing, in addition the distance distribution of the diffusion tree obeys exponential distribution, and the metrics except cascade width decrease after reaching their peak values. When the information diffuses on scale-free (SF) networks, the diffusion trees are similar with the ones on ER networks. In other words, compared with the degree distribution, the infection rate is the main factor of diffusion tree in the same network scale.

中文翻译：

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具有一般度分布的社交网络上的信息扩散结构

在本文中，我们研究了具有一般度分布的社交网络上的信息扩散结构。为了描述信息扩散结构，我们采用了六种不同的度量观点，包括结构病毒性、距离方差、距离变异性、距离敏感性、级联深度和级联宽度。在 Erdös-Rényi (ER) 网络上，我们可以直观地看到，随着扩散树变得越来越密集，扩散树的深度随着感染率的增加先增大到一个峰值然后减小，此外扩散树的距离分布服从指数分布，除级联宽度外的指标在达到峰值后下降。当信息在无标度 (SF) 网络上扩散时，扩散树与 ER 网络上的扩散树相似。换一种说法，