Centrality-Based Connected Dominating Sets for Complex Network Graphs

The author proposes the use of centrality-metrics to determine connected dominating sets (CDS) for complex network graphs. The author hypothesizes that nodes that are highly ranked by any of these four well-known centrality metrics (such as the degree centrality, eigenvector centrality, betweeness centrality and closeness centrality) are likely to be located in the core of the network and could be good candidates to be part of the CDS of the network. Moreover, the author aims for a minimum-sized CDS (fewer number of nodes forming the CDS and the core edges connecting the CDS nodes) while using these centrality metrics. The author discusses our approach/algorithm to determine each of these four centrality metrics and run them on six real-world network graphs (ranging from 34 to 332 nodes) representing various domains. The author observes the betweeness centrality-based CDS to be of the smallest size in five of the six networks and the closeness centrality-based CDS to be of the smallest size in the smallest of the six networks and incur the largest size for the remaining networks.