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