Abstract
The purpose of this article is to present new undirected bigraph generator. Bigraphs (or bipartite graphs) contain nodes of two types and there exist edges only between nodes of different types. This data structure can be observed in various real-life scenarios. Random generator can be used to describe and better understand the scenarios. Moreover, the generator can output a wide range of synthetic datasets. We believe that the datasets can be utilized to evaluate performance of various algorithms that are deployed in such settings. The generative procedure is based on the preferential attachment principle. The principle is combined with the iterative growth mechanism and results in the power-law node degree distribution. Our algorithm extends the classic Barabási - Albert model. We obtain the same scaling exponent as in the classic model, when we set equal parameters for both modalities. However, when we abandon the symmetry we are able to build graphs with wider spectrum of scaling exponents.
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Chojnacki, S., Kłopotek, M.A. (2012). Scale Invariant Bipartite Graph Generative Model. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds) Security and Intelligent Information Systems. SIIS 2011. Lecture Notes in Computer Science, vol 7053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25261-7_19
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DOI: https://doi.org/10.1007/978-3-642-25261-7_19
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