Random graph generative model for Folksonomy network structure approximation

The analysis of social networks has received much attention in recent years. Most social structures are represented as unipartite graphs or bipartite affiliation networks. However, more complex topologies are becoming popular within social networking community. An example of such structure is a Folksonomy: a tuple of connections among users, resources and tags. An intuitive way to represent a Folksonomy is a three-mode hypergraph. It has been shown that in such graphs a clustering coefficient decreases slowly over time at very high level and this property is unachievable for simple random hypergraphs. In this article we represent a Folksonomy as a tripartite graph. This small change of perspective enables us to divide graph generation process into two steps and adapt algorithms used for bipartite graph generation at each step. As a result we obtain iteratively graphs that reflect both dynamics and high level of clustering coefficient