Real-World Implementations of Network Centrality Algorithms across Various

The tutorial analyses how and in what ways the algorithms are used and can be employed in different fields and in the scientific context. This paper revisits the centrality measures; degree, betweenness, closeness and eigenvector centrality and how they are used to study complex networks from social networks, to biology, economy and telecommunications. From the couple of cases and examples, the article shows how these algorithms are utilized to find these vital nodes, improve network performance as well as decision making. Pros and Cons Each of the methods and results evidence the flexibility of centrality measures in enhancing the knowledge and analysis of complex systems. However, issues of scaling these algorithms to operational networks and environments are still explicit. The need for tailor-made centrality analysis methods of scalability and adaptiveness are also outlined for future research agendas in this review.