• Degree assortativity : Degree assortativity measures the extent to which nodes of similar degree connect to one another. It is usually operational- ized as a correlation measure, R$_{k}$ , which measures the Pearson correlation of the stub degrees of all edges (Newman, 2002). • Clustering coefficient : A node’s clustering coefficient measures the density of a node’s neighborhood. Phrased differently, clustering coefficient it measures the fraction of a nodes’ neighbors that are connected to one another. If t$_{u}$ = 1 2 $^{∑}$$_{vw}$a$_{uv}$ a$_{uw}$a$_{vw}$ is the number of triangles (connected neighbors) surrounding node u , then u ’s clustering coefficient is equal to c$_{u}$ = 2 · t$_{u}$ k$_{u}$ ( k$_{u}$ − $_{1)}$. The mean clustering coefficient of a network is simply the average of c$_{u}$ over all possible u . • Characteristic path length : The shortest path matrix, D = [ d$_{uv}$ ], mea- sures the length of the shortest paths between all pairs of u and v . The characteristic path length is the average length of all shortest paths and is calculated as L = ∑ u,v = u d$_{uv}$ n ( n − $_{1)}$. • Network diameter : A network’s diameter is the longest shortest path be- tween any two nodes, and is calculated as max ( d$_{uv}$ ). • Global efficiency : A network’s efficiency is closely related to characteristic path length. Rather than calculating the average length of a shortest path, efficiency is calculated as the average length of 1 $_{d$_{uv}$}$. Specifically, a network’s efficiency is calculated as E = ∑ u,v = u d − 1 uv n ( n − $_{1)}$. • Modularity : Many network measures describe a network’s organization at the level of individual nodes or with a global summary statistic. Al- ternatively, it is possible to describe a network’s ”large-scale structure” (Newman, 2012) - i.e. its organization at an intermediate scale. Perhaps the most common type of large-scale structure is known as a network’s 27