Table 3.
Description of network level metrics.
| Network Metrics | Description |
|---|---|
| Number of Nodes | Number of individuals composing the network |
| Number of Edges | Number of relations (interactions) between individuals |
| Average Degree | The average degree is the mean of the degrees of all nodes in a network |
| Avg. Weighted Degree | Average sum of weights of the edges of nodes |
| Network Diameter | The diameter is given by the maximum eccentricity of the set of vertices in the network. Sparser networks have generally greater diameter than full matrices, due to the existence of fewer paths between pairs of nodes. This metric gives an idea about the proximity of pairs of nodes in the network, indicating how far two nodes are, in the worst of cases |
| Graph Density | Density can explain the general level of connectedness in a network. It is given by the proportion of edges in the network relative to the maximum possible number of edges. It goes from a minimum of 0, when a network has no edges at all, to a maximum of 1, when the network is perfectly connected (also called complete graph or clique) |
| Modularity | Modularity metrics strength of division of a network into communities (modules, clusters). Metrics takes values from range < − 1, 1 > . Value close to 1 indicates strong community structure. When Q = 0 then the community division is not better than random |
| Connected Components | Connected components refer to a set of vertices that are connected to each other by direct or indirect paths. In other words, a set of vertices in a graph is a connected component if every node in the graph can be reached from every other node in the graph |
| Avg. Cluster Coefficient | The local clustering of each node is the fraction of triangles that actually exist over all possible triangles in its neighbourhood. Roughly speaking it tells how well connected the neighbourhood of the node is. If the neighbourhood is fully connected, the clustering coefficient is 1 and a value close to 0 means that there are hardly any connections in the neighbourhood. The average clustering coefficient of a graph is the mean of local clustering |
| Avg. Path Length | Average path length is a concept in network topology that is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network |