Social networks form the basis of many studies in sociology, epidemiology, economics, information science, and social psychology, amongst others. Following the recent surge in the popularity of social networks on the World Wide Web, a need has arisen to model such networks, either numerically or analytically.
In social networks, it has been found that the distribution of the degree of a node is highly skewed, with a small number of nodes having an unusually high degree. These nodes are sometimes called hubs and serve as a good indication that the network is scale-free. Because of this property, traditional random graph models do not describe social networks accurately.
We analyze a number of existing random graph models to determine their relevance and efficiency in modeling current social networks. We also propose a community-based simulation model, aimed at resolving some of the issues with existing random graph models for social networks.
Leendert Botha is a MSc Computer Science student at the MIH Media Lab Stellenbosch University.