The dynamics of social networks is a complex process, as there are many factors which contribute to the formation and evolution of social links. While certain real-world properties are captured by the degree-driven preferential attachment model, it still cannot fully explain social network dynamics. Indeed, important properties such as dynamic community formation, link weight evolution, or degree saturation cannot be completely and simultaneously described by state of the art models. In this paper, we explore the distribution of social network parameters and centralities and argue that node degree is not the main attractor of new social links. Consequently, as node betweenness proves to be paramount to attracting new links - as well as strengthening existing links -, we propose the new Weighted Betweenness Preferential Attachment (WBPA) model, which renders quantitatively robust results on realistic network metrics. Moreover, we support our WBPA model with a socio-psychological interpretation, that offers a deeper understanding of the mechanics behind social network dynamics.

中文翻译：

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加权中间偏好依恋：一种解释社会网络形成和演化的新机制。

社交网络的动态是一个复杂的过程，因为有许多因素导致社交联系的形成和发展。尽管某些现实世界的属性是由度驱动的优先依恋模型捕获的，但它仍然不能完全解释社交网络的动态。确实，重要的属性（例如动态社区形成，链接权重演化或程度饱和度）无法通过最新模型完全完整地描述。在本文中，我们探索了社交网络参数和中心的分布，并认为节点度不是新社交链接的主要吸引者。因此，由于节点之间的相互关系对于吸引新链接以及增强现有链接至关重要，因此，我们提出了新的加权中间人优先依附关系（WBPA）模型，从而根据实际的网络指标得出定量可靠的结果。此外，我们以一种社会心理学的解释来支持我们的WBPA模型，该模型提供了对社交网络动态背后机制的更深刻理解。