Closeness Centrality in Neural and Interconnection Networks

In graph theory and network analysis, centrality measures identify the most important vertices within a graph. In a connected graph, closeness centrality of a node is a measure of centrality, calculated as the reciprocal of the sum of the lengths of the shortest paths between the node and all other nodes in the graph. In this paper, we compute closeness centrality for a class of neural networks and the sibling trees, classified as a family of interconnection networks.