I am looking for a way, to predict a numerical property of a node.
My Neo4j contains a supply chain network, I am looking for a way to add missing properties to product nodes, especially a price. The price should be predicted from the context. For example, through considering the price of the predecessors and successors or a different similar product in the same location...
I already extracted some of that information into a table and used traditional machine learning algorithms for the same task. The results were quite promising, now I want to improve on that by considering the complete graph context.
Some more information: My Neo4j contains no transactional data and it is not dynamical.
Thank you for every idea or suggestion in advance!
Okay, so basically what I have is a rather complex Neo4j displaying a logisitic network. In this, the nodes for product have as property a certain price. The property is missing in approximately 10% of all product nodes. I am looking for a Machine Learning Algorithm to predict the price for those Nodes where it is missing. Specifially an algorithm that leverages the context of the graph database to make its prediction.
For example a product with similar predecessors, followers, properties and so on, should have a similar price.
I hope that helped!
Would node similarity algorithm go in the right direction?
You can use it to measure "context" similarity between your nodes.
If this is not sufficient to make a prediction, you can use similarity in a more classical ML regression algorithm, as a feature, together with the node properties.
I get you . you can try this user defined algorithm by Lauren Shin https://towardsdatascience.com/graphs-and-ml-multiple-linear-regression-c6920a1f2e70. Basicall She users Logistic Linear regression to predict node properties basing on similar nodes inside neo4j.