Bipartite Random Graph for Python

The Bipartite Random Graph (BiRG) is a statistical null model for binary bipartite networks. It offers an unbiased method of analyzing node similarities and obtaining statistically validated monopartite projections [Saracco2016].

The BiRG is an extension of the Erdős–Rényi model [ER] to bipartite graphs. Link probabilities are obtained by constraining the total number of links between the two layers in the network. Similar null-models, which are based on the principle of entropy maximization, are

BiCM - Bipartite Configuration Model
BiPCM - Bipartite Partial Configuration Model

Please consult the original articles for details about the underlying methods and applications to user-movie and international trade databases [Saracco2016, Straka2016].

Author

Mika J. Straka

Version and Documentation

The newest version of the module can be found on https://github.com/tsakim/birg.

How to cite

If you use the birg module, please cite its location on Github https://github.com/tsakim/birg and the original article [Saracco2016].

References

[ER] P. Erdős, A. Rényi, On Random Graphs. I, Publicationes Mathematicae, 6, 290–297 (1959)

[Saracco2016] F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, arXiv preprint arXiv:1607.02481

[Straka2016] M. J. Straka, F. Saracco, G. Caldarelli, Product Similarities in International Trade from Entropy-based Null Models, Complex Networks 2016, 130-132 (11 2016), ISBN 978-2-9557050-1-8