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a. Centrality measures

Centrality measures are a significant indicator used in network analysis. There are different types of centrality measures. Some prominent measures are given as follows:

 – Betweenness centralityAssuming the important nodes connect other nodes. The betweenness centrality is defined as the cumulative sum of ratios of the paths between two nodes through a node to the total number of shortest paths available between those nodes.

 – Closeness centralityAssuming in a connected graph, closeness centrality is a measure of centrality in the given network. The node is closer to all nodes if it is more central.

 – Degree centralityAssuming the networks where all nodes are connected and one or more than one nodes have predominant connections in comparison with other neighbouring nodes For instance, in an undirected graph, the degree centrality is defined by the number of connections attached to each node.

 – Eigenvector centralityAssuming the networks where all nodes are connected and one or more than one nodes have predominant connections in comparison with other neighboring nodes. Eigenvector centrality is an algorithm that measures the influence or connectivity of nodes [2]. Relationships to high-scoring nodes contribute more to the score of a node than connections to low-scoring nodes. A high score means that a node is connected to other nodes that have high scores.

 – PageRank centralityAssuming the networks where all nodes are connected and one or more than one node have predominant connections in comparison with other neighboring nodes. For instance, nodes relate to links representing appropriate weights and weights are updated when the node centrality/significance changes in the directed network [3].

b. Geodesic distance

c. Networks

 – Distributed

 – Centralized

 – Decentralized