Betweenness centrality is a distance-based invariant of graphs. In this paper, we use lexicographic product to compute betweenness centrality of some important classes of graphs. Finally, we pose some open problems related to this topic.

Betweenness centrality is a quantity that is frequently used to measure how ‘central’ a vertex v is. It is defined as the sum, over pairs of vertices other than v, of the proportions of shortest paths that pass through v. In this paper, we study the distribution of the betweenness centrality in random trees and related, subcritical graph families. Specifically, we prove that the betweenness cen...

A novel algorithm for the fast detection of hubs in chemical networks is presented. The algorithm identifies a set of nodes in the network as most significant, aimed to be the most effective points of distribution for fast, widespread coverage throughout the system. We show that our hubs have in general greater closeness centrality and betweenness centrality than vertices with maximal degree, w...

a novel algorithm for the fast detection of hubs in chemical networks is presented. thealgorithm identifies a set of nodes in the network as most significant, aimed to be the mosteffective points of distribution for fast, widespread coverage throughout the system. we showthat our hubs have in general greater closeness centrality and betweenness centrality thanvertices with maximal degree, while...

A class of centrality measures called betweenness centralities reflects degree of participation of edges or nodes in communication between different parts of the network. The original shortest-path betweenness centrality is based on counting shortest paths which go through a node or an edge. One of shortcomings of the shortest-path betweenness centrality is that it ignores the paths that might ...

this research examines the association between co-authorship network centrality (degree, closeness, betweenness, eigenvector, bonacich flow betweenness) and productivity of information science researchers. the research population includes all those researchers who have published at least one record in one of the twenty journals of information science which has an impact factor of 0.635 as a min...

Betweenness centrality is a centrality measure that is widely used, with applications across several disciplines. It is a measure which quantifies the importance of a vertex based on its occurrence in shortest paths between all possible pairs of vertices in a graph. This is a global measure, and in order to find the betweenness centrality of a node, one is supposed to have complete information ...

Processing large graphs is an emerging and increasingly important computation in a variety of application domains, from social networking to genomics and marketing. One of the important and computationally challenging structural graph metrics is node betweenness centrality, a measure of influence of a node in the graph. The best known algorithm for computing exact betweenness centrality runs in...