With user-defined procedures there is unlimited potential for analysis of graph data in Neo4j.
As you move from relational to graph database, you need not leave your most trusted statistical methods behind. Linear and logistic regression can be implemented with updating formulas so that the data and design of the model is as flexible as the graph database itself. We demonstrate that a price predictor, built with linear regression, is a seamless addition to a graph database that contains short term housing rental data from Austin, TX.
Lauren Shin, Developer Relations, Neo4j
#MachineLearning #GraphAlgorithms #GraphConnect