Bridging the gap between different social networks

The available social network models that exist today were designed primarily on the basis of the analysis of statistical properties and structural features, as well as the physical or social distances between individuals of social systems, which sometimes is not sufficient because the structure of some social networks is closely tied to individuals' social identities. In addition, the difference in growth speed between different social networks is also neglected in these models. We propose a synthetic model that involves social identity and adjustable growth speed factors to compensate for these limitations. The model features four types of node connection mechanisms: random attachment, transitive attachment, preferential attachment and anti-preferential attachment. Experimental results indicate that the model can not only produce rich topological structures but can also match real social networks well in both their macro properties and their micro foundations. Thus, the model is helpful in understanding both the evolution of social networks and the differences and similarities among different social networks.