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Table 2 Social network metrics used in this research

From: Heuristic methods for synthesizing realistic social networks based on personality compatibility

Metric Definition
Nodes Number of nodes in the network; here denoted n.
Links Number of links in the network; here denoted m.
Components Number of disjoint sets of connected nodes in a network.For a connected network, the value of this metric is 1.
Network density Number of links in the network divided by the number of possible links n · (n – 1) / 2; here denoted p.
Average degree Average, or mean, of the nodes’ degrees.
Standard deviation degree Standard deviation of the nodes’ degrees.
Global clustering coefficient Ratio of closed nodes of vertices to connected triplets of nodes.
Average clustering coefficient Average of the nodes’ local clustering coefficients;the latter is the ratio of actual links to neighborsto possible links to neighbors for a given node.
Number of communities Number of clusters in the network
Cluster Gini coefficient Inequality of distribution of nodes among communities
Mean path length Mean of the number of links in the shortest path betweeneach pair of nodes.
Average betweenness Mean of the nodes’ betweenness centrality values, which is the number of shortest paths between pairs of node that pass through a node.
Maximum betweenness Maximum of the nodes’ betweenness centrality values.
Average closeness Mean of the nodes’ closeness centrality values, which is the sum of the path lengths between the node and all other nodes.
Minimum closeness Minimum of the nodes’ closeness centrality values.
Average eigencentrality Mean of the nodes’ eigencentrality (also known as eigenvector centrality); the latter is a measure of the number of links each of a nodes neighbors have.
Minimum eigencentrality Minimum of the nodes’ eigencentrality.
Network radius Minimum of the nodes’ eccentricities; the latter is the maximum length of the shortest paths from a node to all other nodes.
Average eccentricity Mean of the nodes’ eccentricities.
Network diameter Maximum of the nodes’ eccentricities.