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The purpose of this article is to introduce a new iterative algorithm withproperties resembling real life bipartite graphs. The algorithm enables us togenerate wide range of random bigraphs, which features are determined by a setof parameters.We adapt the advances of last decade in unipartite complexnetworks modeling to the bigraph setting. This data structure can be observedin several situations. However, only a few datasets are freely available totest the algorithms (e.g. community detection, influential nodesidentification, information retrieval) which operate on such data. Therefore,artificial datasets are needed to enhance development and testing of thealgorithms. We are particularly interested in applying the generator to theanalysis of recommender systems. Therefore, we focus on two characteristicsthat, besides simple statistics, are in our opinion responsible for theperformance of neighborhood based collaborative filtering algorithms. Thefeatures are node degree distribution and local clustering coeficient.

Random Graph Generator for Bipartite Networks Modeling