$C_{\mathcal{I}}$ of its population $\mathcal{P}_{\mathcal{I}}$ included in a single cluster obtained at $p - \Delta p$. $C$ is then calculated as the average of $C_{\mathcal{I}}$ weighted over the relative populations $\mathcal{P}_{\mathcal{I}} / \sum_{\mathcal{I}} \mathcal{P}_{\mathcal{I}}$. In this way, the maximum value of $C$ is always 1, which corresponds to a perfectly consistent clusterization. The minimum of $C$ at a given $p$ is $1/n$, where $n$ is the number of clusters generated at $p -\Delta p$. To make the value of $C$ independent of $n$, we normalize it such that $1/n$ corresponds to 0 and rescale the $[1,1/n]$ segment linearly to $[1,0]$.

Practical advices for the parameters choice
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Network creation and clusterization depend on two parameters: the similarity cutoff $S^{*}$ which sets the accepted degree of similarity between different structures and the granularity parameter $p$ which determines the degree of coarse-graining in the clusterization. It is important to note that finding the correct value of these parameters for structural comparison is an open and unsolved problem in the broader field of complex systems. Apparently, there is no single [*right*]{} choice, as those parameters probe the system at different resolutions. Best practice suggests a scanning in parameter space in order to asses 