Random graph generator for bipartite networks modeling

The purpose of this article is to introduce a new bipartite graph generation algorithm. Bipartite graphs consist of two types of nodes and edges join only nodes of different types. This data structure appears in various applications (e.g. recommender systems or text clustering). Both real-life datasets and formal tools enable us to evaluate only a limited set of properties of the algorithms that are used in such situations. Therefore, artificial datasets are needed to enhance development and testing of the algorithms. Our generator can be used to produce a wide range of synthetic datasets.