Correlation analysis of node and edge centrality measures in artificial complex networks

Abstract

The importance of a node in a social network is identified through a set of measures called centrality. Degree centrality, closeness centrality, betweenness centrality and clustering coefficient are the most frequently used metrics to compute node centrality. Their computational complexity in some cases makes unfeasible, when not practically impossible, their computations. For this reason we focused on two alternative measures, WERW-Kpath and Game of Thieves, which are at the same time highly descriptive and computationally affordable. Our experiments show that a strong correlation exists between WERW-Kpath and Game of Thieves and the classical centrality measures. This may suggest the possibility of using them as useful and more economic replacements of the classical centrality measures.

References in corpus (10)

• Emergence of scaling in random networks
• Renormalization group analysis of the small-world network model
• Growing Scale-Free Networks with Tunable Clustering
• Generalizations of the clustering coefficient to weighted complex networks
• Consistency and differences between centrality measures across distinct classes of networks
• A Novel Measure of Edge Centrality in Social Networks
• Mixing local and global information for community detection in large networks
• Enhancing community detection using a network weighting strategy
• Disrupting Resilient Criminal Networks through Data Analysis: The case of Sicilian Mafia
• Correlations among Game of Thieves and other centrality measures in complex networks