NAME
compute_tau.py - compute the Kendall’s rank correlation coefficient τb between two rankings.
SYNOPSYS
compute_tau.py <file1> <file2>
DESCRIPTION
Compute the Kendall’s rank correlation coefficient τb between two rankings provided in the input files file1 and file2. Each input file contains a list of lines, where the n-th line contains the value of rank of the n-th node. For instance, file1 and file2 might contain the ranks of nodes induced by the degree sequences of two distinct layers of a multiplex.
However, the program is pretty general and can be used to compute the Kendall’s rank correlation coefficient between any generic pair of rankings.
N.B.: This implementation takes properly into account rank ties.
OUTPUT
The program prints on stdout the value of the Kendall’s rank correlation coefficient τb between the two rankings provided as input.
REFERENCE
V. Nicosia, V. Latora, “Measuring and modeling correlations in multiplex networks”, Phys. Rev. E 92, 032805 (2015).
Link to paper: http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.032805
V. Nicosia, G. Bianconi, V. Latora, M. Barthelemy, “Growing multiplex networks”, Phys. Rev. Lett. 111, 058701 (2013).
Link to paper: http://prl.aps.org/abstract/PRL/v111/i5/e058701
V. Nicosia, G. Bianconi, V. Latora, M. Barthelemy, “Non-linear growth and condensation in multiplex networks”, Phys. Rev. E 90, 042807 (2014).
Link to paper: http://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.042807