Emergence and evolution of social networks through exploration of the adjacent possible space

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.Understanding how social interactions between individuals shape the large-scale properties of social networks is key to understanding our society. Here, the authors revisit the Adjacent Possible paradigm to describe the evolution of social networks as a social space exploration process, presenting a simple model that reproduces several empirical features common to diverse real-world social networks.