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<h5 class="subsubsectionHead"><a
id="x35-340001.2.2"></a><span
class="cmtt-10x-x-109">compute</span><span
class="cmtt-10x-x-109">_tau.py</span></h5>
<!--l. 3--><p class="noindent" ><span
class="cmbx-10x-x-109">NAME</span>
<!--l. 3--><p class="indent" > <span
class="cmbx-10x-x-109">compute</span><span
class="cmbx-10x-x-109">_tau.py </span>- compute the Kendall’s rank correlation coefficient <span
class="cmmi-10x-x-109">τ</span><sub><span
class="cmmi-8">b</span></sub>
between two rankings.
<!--l. 3--><p class="noindent" ><span
class="cmbx-10x-x-109">SYNOPSYS</span>
<!--l. 3--><p class="indent" > <span
class="cmbx-10x-x-109">compute</span><span
class="cmbx-10x-x-109">_tau.py </span><span
class="cmmi-10x-x-109"><</span><span
class="cmitt-10x-x-109">file1</span><span
class="cmmi-10x-x-109">> <</span><span
class="cmitt-10x-x-109">file2</span><span
class="cmmi-10x-x-109">></span>
<!--l. 19--><p class="noindent" ><span
class="cmbx-10x-x-109">DESCRIPTION</span>
<!--l. 19--><p class="indent" > Compute the Kendall’s rank correlation coefficient <span
class="cmmi-10x-x-109">τ</span><sub><span
class="cmmi-8">b</span></sub> between two rankings
provided in the input files <span
class="cmti-10x-x-109">file1 </span>and <span
class="cmti-10x-x-109">file2</span>. 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, <span
class="cmti-10x-x-109">file1</span>
and <span
class="cmti-10x-x-109">file2 </span>might contain the ranks of nodes induced by the degree sequences of
two distinct layers of a multiplex.
<!--l. 19--><p class="indent" > However, the program is pretty general and can be used to compute
the Kendall’s rank correlation coefficient between any generic pair of
rankings.
<!--l. 19--><p class="indent" > N.B.: This implementation takes properly into account rank ties.
<!--l. 24--><p class="noindent" ><span
class="cmbx-10x-x-109">OUTPUT</span>
<!--l. 24--><p class="indent" > The program prints on <span
class="cmtt-10x-x-109">stdout </span>the value of the Kendall’s rank correlation
coefficient <span
class="cmmi-10x-x-109">τ</span><sub><span
class="cmmi-8">b</span></sub> between the two rankings provided as input.
<!--l. 31--><p class="noindent" ><span
class="cmbx-10x-x-109">REFERENCE</span>
<!--l. 31--><p class="indent" > V. Nicosia, V. Latora, “Measuring and modeling correlations in multiplex
networks”, <span
class="cmti-10x-x-109">Phys. Rev. E </span><span
class="cmbx-10x-x-109">92</span>, 032805 (2015).
<!--l. 31--><p class="indent" > Link to paper: <a
href="http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.032805" class="url" ><span
class="cmtt-10x-x-109">http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.032805</span></a>
<!--l. 31--><p class="indent" > V. Nicosia, G. Bianconi, V. Latora, M. Barthelemy, “Growing multiplex
networks”, <span
class="cmti-10x-x-109">Phys. Rev. Lett. </span><span
class="cmbx-10x-x-109">111</span>, 058701 (2013).
<!--l. 31--><p class="indent" > Link to paper: <a
href="http://prl.aps.org/abstract/PRL/v111/i5/e058701" class="url" ><span
class="cmtt-10x-x-109">http://prl.aps.org/abstract/PRL/v111/i5/e058701</span></a>
<!--l. 31--><p class="indent" > V. Nicosia, G. Bianconi, V. Latora, M. Barthelemy, “Non-linear growth and
condensation in multiplex networks”, <span
class="cmti-10x-x-109">Phys. Rev. E </span><span
class="cmbx-10x-x-109">90</span>, 042807 (2014).
<!--l. 31--><p class="indent" > Link to paper: <a
href="http://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.042807" class="url" ><span
class="cmtt-10x-x-109">http://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.042807</span></a>
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