Calculating Closeness Centrality of Nodes in Complex Networks

In complex network analysis, closeness centrality is a crucial metric for nodes. It effectively reflects the importance of each node within the network structure. We can develop a MATLAB M-file to calculate node closeness centrality in complex networks, thereby enabling better investigation of network characteristics. This M-file implementation requires processing the network's adjacency matrix and computing the closeness centrality metric for each node. The algorithm typically involves calculating the shortest path distances from each node to all other nodes using functions like graphshortestpath or implementing Dijkstra's algorithm, then deriving centrality values as reciprocal sums of these distances. In practical applications, additional factors should be considered alongside closeness centrality, such as node degree, clustering coefficient, and betweenness centrality, to obtain comprehensive network insights. The implementation may include functions for adjacency matrix validation, distance matrix computation, and centrality normalization. By computing node closeness centrality, researchers can gain deeper understanding of complex network structures and properties, facilitating applications in social network analysis, biological networks, and infrastructure systems.