Closeness Centrality of Corona Product between Well-Known Graph and General Graph.

Centrality measurement plays an important role to identify important/influential vertices and edges in a network or graph from different points of view. It also provide invaluable insights into the structure and functioning of interconnected systems, enabling researchers to identify critical nodes for targeted interventions, predict network behaviors, and optimize network performance. Though there are different centrality measurements in graphs theory, yet closeness centrality is widely used to analyze biological networks, social networks, fuzzy social network, transportation networks, etc. The closeness centrality of a node x in a network/graph is the unit fraction whose denominator is the sum of the distances from x to other nodes. This paper presents theoretical development to compute the closeness centrality of each node/vertex of different corona product graphs between well known graph (path graph, complete graph, cycle graph, wheel graph, star graph and complete bipartite graph) and general graph. Corona graph has lots of applications in signed networks, biotechnology, chemistry, small-world network, etc. We also present a suitable real application of our proposed results by which we can identify the influential nodes in small-world network. [ABSTRACT FROM AUTHOR]