Gremlin link prediction queries using link-prediction models in Neptune ML
Link-prediction models can solve problems such as the following:
Head-node prediction: Given a vertex and an edge type, what vertices is that vertex likely to link from?
Tail-node prediction: Given a vertex and an edge label, what vertices is that vertex likely to link to?
Note
Edge prediction is not yet supported in Neptune ML.
For the examples below, consider a simple graph with the vertices User
and Movie that are linked by the edge Rated.
Here is a sample head-node prediction query, used to predict the top five
users most likely to rate the movies, "movie_1", "movie_2",
and "movie_3":
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .with("Neptune#ml.limit", 5) .V("movie_1", "movie_2", "movie_3") .in("rated").with("Neptune#ml.prediction").hasLabel("user")
Here is a similar one for tail-node prediction, used to predict the top five movies
that user "user_1" is likely to rate:
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("user_1") .out("rated").with("Neptune#ml.prediction").hasLabel("movie")
Both the edge label and the predicted vertex label are required. If either is omitted, an exception is thrown. For example, the following query without a predicted vertex label throws an exception:
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("user_1") .out("rated").with("Neptune#ml.prediction")
Similarly, the following query without an edge label throws an exception:
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("user_1") .out().with("Neptune#ml.prediction").hasLabel("movie")
For the specific error messages that these exceptions return, see the list of Neptune ML exceptions.
Other link-prediction queries
You can use the select() step with the as() step to
output the predicted vertices together with the input vertices:
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("movie_1").as("source") .in("rated").with("Neptune#ml.prediction").hasLabel("user").as("target") .select("source","target") g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("user_1").as("source") .out("rated").with("Neptune#ml.prediction").hasLabel("movie").as("target") .select("source","target")
You can make unbounded queries, like these:
g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("user_1") .out("rated").with("Neptune#ml.prediction").hasLabel("movie") g.with("Neptune#ml.endpoint","node-prediction-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V("movie_1") .in("rated").with("Neptune#ml.prediction").hasLabel("user")
Using inductive inference in a link prediction query
Supposing you were to add a new node to an existing graph, in a Jupyter notebook, like this:
%%gremlin g.addV('label1').property(id,'101').as('newV1') .addV('label2').property(id,'102').as('newV2') .V('1').as('oldV1') .V('2').as('oldV2') .addE('eLabel1').from('newV1').to('oldV1') .addE('eLabel2').from('oldV2').to('newV2')
You could then use an inductive inference query to predict the head node, taking into account the new node:
%%gremlin g.with("Neptune#ml.endpoint", "lp-ep") .with("Neptune#ml.iamRoleArn", "arn:aws:iam::123456789012:role/NeptuneMLRole") .V('101').out("eLabel1") .with("Neptune#ml.prediction") .with("Neptune#ml.inductiveInference") .hasLabel("label2")
Result:
==>V[2]
Similarly, you could use an inductive inference query to predict the tail node, taking into account the new node:
%%gremlin g.with("Neptune#ml.endpoint", "lp-ep") .with("Neptune#ml.iamRoleArn", "arn:aws:iam::123456789012:role/NeptuneMLRole") .V('102').in("eLabel2") .with("Neptune#ml.prediction") .with("Neptune#ml.inductiveInference") .hasLabel("label1")
Result:
==>V[1]
