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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加权介数偏好依恋：解释社交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，因为有许多因素有助于社会联系的形成和演变。虽然学位驱动的偏好依恋模型捕获了某些现实世界的属性，但它仍然无法完全解释社交网络动态。事实上，最先进的模型无法完全、同时地描述动态社区形成、链接权重演化或饱和度等重要属性。在本文中，我们探讨了社交网络参数和中心性的分布，并认为节点度并不是新社交链接的主要吸引因素。因此，由于节点介数被证明对于吸引新链接以及加强现有链接至关重要，因此我们提出了新的加权介数优先附件（WBPA）模型，该模型可以根据实际网络指标提供定量稳健的结果。此外，我们通过社会心理学解释来支持 WBPA 模型，这可以更深入地理解社交网络动态背后的机制。