Bipartite Configuration Model for Python
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README.md

Bipartite Configuration Model for Python

The Bipartite Configuration Model (BiCM) is a statistical null model for binary bipartite networks [Squartini2011, Saracco2015]. It offers an unbiased method for analyzing node similarities and obtaining statistically validated monopartite projections [Saracco2017].

The BiCM belongs to a series of entropy-based null models for binary bipartite networks, see also

  • BiPCM - Bipartite Partial Configuration Model
  • BiRG - Bipartite Random Graph

Please consult the original articles for details about the underlying methods and applications to user-movie and international trade databases [Saracco2017], [Straka2017].

Author

Mika J. Straka

Version and Documentation

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

The complete documentation is available at http://bicm.readthedocs.io/ and in the file docs/BiCM_manual.pdf

How to cite

If you use the bicm module, please cite its location on Github https://github.com/tsakim/bicm and the original articles [Saracco2015] and [Saracco2017].

References

[Saracco2015] F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015).

[Saracco2017] F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)

[Squartini2011] T. Squartini, D. Garlaschelli, Analytical maximum-likelihood method to detect patterns in real networks, New Journal of Physics 13, (2011)

[Straka2017] M. J. Straka, G. Caldarelli, F. Saracco, Grand canonical validation of the bipartite international trade network, Phys. Rev. E 96, 022306 (2017)


Copyright (c) 2015-2017 Mika J. Straka