Power-law node degree distribution in online affiliation networks

The purpose of this article is to present a preferential attachment model adjusted to generation of bipartite graphs. The original model is able to produce unipartite graphs with node degree distribution following power law relation. The motivation for extending classic model is the fact that multi-partite graph topologies are becoming more and more popular in social networks. This phenomenon raises questions about our ability to transform models that describe unipartite graphs to the new settings. In some cases bipartite structure is given explicitly, in other cases the bipartite structure may be induced from more complex topologies. We present both empirical results concerning node degree distribution in real-life bipartite networks and a modified preferential attachment model that is able to reflect the properties observed in these networks.