Gremlin inference queries in Neptune ML

As described in Neptune ML capabilities, Neptune ML supports training models that can do the following kinds of inference tasks:

We can illustrate these different tasks with examples that use the MovieLens 100k dataset provided by GroupLens Research. This dataset consists of movies, users, and ratings of the movies by the users, from which we've created a property graph like this:

Node classification: In the dataset above, Genre is a vertex type which is connected to vertex type Movie by edge included_in. However, if we tweak the dataset to make Genre a categorical feature for vertex type Movie, then the problem of inferring Genre for new movies added to our knowledge graph can be solved using node classification models.

Node regression: If we consider the vertex type Rating, which has properties like timestamp and score, then the problem of inferring the numerical value Score for a Rating can be solved using node regression models.

Edge classification: Similarly, for a Rated edge, if we have a property Scale that can have one of the values, Love, Like, Dislike, Neutral, Hate, then the problem of inferring Scale for the Rated edge for new movies/ratings can be solved using edge classification models.

Edge regression: Similarly, for the same Rated edge, if we have a property Score that holds a numerical value for the rating, then this can be inferred from edge regression models.

Link prediction: Problems like, find the top ten users who are most likely to rate a given movie, or find the top ten Movies that a given user is most likely to rate, falls under link prediction.