Table 1 One sentence definition for each of the graph properties.
Measure | Easy-to-understand description |
|---|---|
Degree Distribution (Pk) | Refers to the probability distribution of the degrees (number of connections) of nodes in a graph |
Degree Centrality (DC) | Quantifies the number of direct connections a node has within a graph |
Closeness Centrality (CC) | Quantifies how close a node is to all other nodes in the graph in terms of the shortest paths |
Eigenvector Centrality (EC) | Assigns importance to a node based on both its direct connections and the importance of those nodes it is connected to |
Betweenness Centrality (BC) | Quantifies the extent to which a node lies on the shortest paths between other pairs of nodes, highlighting its potential influence in controlling information flow |
Gravity Index Centrality (GIC) | Is based on the universal gravity concept, which considers both neighbors’ node’ influences and path information in the graph |
Clustering Coefficient | Quantifies the degree to which nodes in a graph tend to form clusters or tightly interconnected groups |
Jaccard Similarity Coefficient | Quantify the similarity between two sets of nodes by dividing the size of their intersection by the size of their union |
Shannon Entropy | Quantifies the uncertainty or information content of node attributes, providing insights into the diversity or heterogeneity within the graph |
Diameter | is the maximum shortest path length between any pair of nodes in a graph, indicating the longest distance or number of steps required to travel between nodes |
Transitivity | Quantifies the likelihood that if node A is connected to node B and node B is connected to node C, then there is also a connection between node A and node C |
Scale-Free | A property of complex graphs where the distribution of node degrees follows a power-law distribution, with a few nodes having a significantly higher number of connections compared to the majority of nodes |
Small-World | A property of a graph characterized by a high level of local clustering, where nodes tend to be connected to their immediate neighbors, along with short average path lengths between any two nodes in the graph |
