Bipartite Configuration Model for Python - Documentation¶
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
Please consult the original articles for details about the underlying methods and applications to user-movie and international trade databases [Saracco2017], [Straka2017].
An example case is illustrated in the Tutorial.
How to cite¶
If you use the
bicm module, please cite its location on Github and the original articles [Saracco2015] and
|[Saracco2015]||(1, 2) 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]||(1, 2, 3) 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)|
- BiCM Quickstart
- Parallel Computation and Memory Management