The interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

人与人之间的互动是我们社会的骨干力量。人们如何建立新的联系并在他们之间分配社交互动可以揭示我们很多社会组织。我们利用最近可能空间的数学形式化来提出一个微观模型，说明社交网络的增长和动态。在个人的层面上，我们的模型正确地再现了人们获得新朋友的比率以及他们如何在现有边缘之间分配他们的互动。在宏观方面，该模型再现了社交网络的关键拓扑和动态特征：程度和活动的广泛分布，平均聚类系数和社区结构。该理论诞生于三个不同的现实世界社交网络中：