Below are some of the use cases for Graph Analytics: Fraud prediction Capital, one uses Spark’s graph analytics capabilities to better understand fraud networks. By using historical fraudulent information (like phone numbers, addresses, or names) they discover fraudulent credit requests or transactions. For instance, any user accounts within two hops of a fraudulent phone number might be considered suspicious. Anomaly detection By looking at how networks of individuals connect with one another, outliers and anomalies can be flagged for manual analysis. For instance, if typically, in our data each vertex has ten edges associated with it and a given vertex only has one edge, that might be worth investigating as something strange. Classification Given some facts about certain vertices in a network,  you can classify other vertices according to their connection to the original node. For instance, if a certain individual is labeled as an influencer in a social network, we could classify other individuals with similar network structures as influencers. Recommendation Google’s original web recommendation algorithm, PageRank, is a graph algorithm that analyses website relationships in order to rank the importance of web pages. For example, a web page that has a lot of links to it is ranked as more important than one with no links to it.