Table 4.
Description of node level metrics.
| Node Level Metrics | Description |
|---|---|
| Eccentricity | The eccentricity measure captures the distance between a node and the node that is furthest from it |
| Closeness Centrality | Closeness centrality is a measure that indicates how close a node is to all the other nodes in a network. A high closeness centrality means that there is a large average distance to other nodes in the network |
| Harmonic Closeness centrality | Harmonic Centrality is a variant of Closeness Centrality, that reverses the sum and reciprocal operations in graphs with unconnected clusters, the harmonic centrality could be a better indicator of centrality than closeness centrality |
| Betweenness Centrality | Betweenness centrality is a measure based on the number of shortest paths between any two nodes that pass through a particular node. Nodes around the edge of the network would typically have a low betweenness centrality. A high betweenness centrality might suggest that the individual is connecting various parts of the network together |
| Degree | The degree of a node is the number of relation (edge) it has. It is the sum of edges for a node |
| Weighted Degree | The weighted degree is based on the number of edges for a node but pondered by the weight of each edge. It is the sum of the weight of the edges |
| Authority | The authority indicates the value of the information that the node holds. The relevance of an authority is “measured” by the number of inward links (or simply by the number of links in undirected graphs) |
| Modularity Class | Modularity class identifies nodes that are more densely connected than to the rest of the network. Those nodes have the same modularity class |
| Component Number | A connected component of an undirected graph is a maximal set of nodes such that a path connects each pair of nodes. The component number identifies a group of nodes that belong to the same components |
| Clustering | Clustering is the fraction of triangles that do 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 |
| Triangles |
Counts the number of triangles for each node in the graph. A triangle is a set of three nodes where each node has a relationship to the other two. In graph theory terminology, this is sometimes referred to as a 3-clique Triangle counting is used to detect communities and measure the cohesiveness of those communities. It is also used to determine clustering coefficients |
| Eigen centrality | Eigenvector centrality is a centrality index that calculates the centrality of a node based not only on their connections, but also based on the centrality of that node’s connections |