This blog series aims to help developers utilize graph analytics and algorithms to innovate and develop intelligent solutions faster using a graph database. Degree Centrality, one of the centrality algorithms, measures the number of incoming and outgoing relationships from a node, helping find popular nodes in a graph. Proposed by Linton C. Freeman in 1979, it is often used as part of global analysis to calculate minimum and maximum degrees across the whole graph. Degree Centrality can be useful for analyzing influence in social networks or detecting fraudsters in online auctions, where individuals with high weighted centrality tend to collude with each other. An example using a small dataset demonstrates how Degree Centrality works, highlighting its application in evaluating near-term risk and probability of information transmission. The algorithm is an important component of any attempt to analyze influence, making it a valuable tool for developers and graph enthusiasts.