Scale Invariant Bipartite Graph Generative Model

  • Szymon Chojnacki
  • Mieczysław A. Kłopotek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)

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.

Keywords

Bipartite Graph Random Graph Degree Distribution Preferential Attachment Scaling Exponent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Szymon Chojnacki
    • 1
  • Mieczysław A. Kłopotek
    • 1
  1. 1.Institute of Computer Science PASWarsawPoland

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