So I have been searching and watching youtube videos on how to enhance ML with features derived from graphs and want to try it out. I have a node that is a person and that person has some properties (age, education, rent_own, etc) and I currently have have connected to one node called 'h1n1_vax_yes' and another node called 'flu_vax_yes'. Both of these connections indicate that the person has taken either one or both of those vaccines. What I am attempting to do is to use a Similarity algo to find how similar person nodes are to each for all the nodes that took the h1n1 vaccine based on the properties, which i have 35 of (similar to what is shown here at 11:00- https://www.youtube.com/watch?v=LWw94LVhfLk&list=WL&index=2&t=651s) . Looking at the examples, it shows the property of the edge being used as weight not the node properties (https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/cosine/). Is there a way to do this or do I have to create a node for all 35 properties and the person? What would be a recommended approach to helping in adding more features to my dataset so that I could improve my predictions?
Perhaps my Google ninja search skills are not up to par in finding this answer....
-Using the latest GDS libs and Neo4j
-Below is basic schema