This blog series aims to help developers better utilize graph analytics and graph algorithms, enabling them to develop intelligent solutions faster using a graph database like Neo4j. The focus is on Centrality algorithms, which measure the importance of nodes in a graph by analyzing their relationships. Betweenness Centrality measures the number of shortest paths that pass through a node, identifying influential nodes that serve as bridges between different clusters. It is used to detect network flow, identify influencers in organizations, and help microbloggers spread their reach on Twitter. The algorithm calculates scores based on the frequency of shortest paths passing through each node and can be affected by assumptions about communication patterns in real-life scenarios. An approximation algorithm called RA-Brandes is used for large graphs due to computational complexity, which considers a subset of nodes with strategies like random or degree-based selection.