Table 1. Complex network statistics commonly used in this work.

Measure	Definition	Interpretation
Node degree		Number of edges connected to a given node i. Nodes with relatively high values of k are called hubs
Shortest path length	where ri↔j is the shortest path between i and j	The number of edges encountered in the shortest path between node i and j
Characteristic path length		Measure of network integration. Small values identify strongly integrated networks
Clustering coefficient	, with	Measure of fine-grain network segregation. It counts the average number of triangles t (3-node fully connected graphs) present in the network
Modularity	, where M is a partition of V (whose elements are called modules) and euv is the proportion of links that connect nodes in module u with nodes in module v	It evaluates the tendency of the network to be reduced in independent (or scarcely dependent) modules
Betweenness Centrality	, where ρhj is the number of shortest paths between h and j, and ρhj(i) is the number of shortest paths between h and j that pass through i	It is the amount of shortest paths that pass through the node i. It roughly indicates how much information burdens the node i
Small-worldness	, where Cr and Lr are the randomized version of the original network; S >1 denotes small-world networks	It determines how much the network is a small-world network.

All formulas are referred to a (undirected) graph 〈V, E〉, with |V| = N, opportunely described by the adjacency N × N-matrix A = a_ij where a_ij = 1 if and only if there exist the element (i, j) in the set E and 0 otherwise.