Amazon Neptune ML for machine learning on graphs - Amazon Neptune
Neptune ML capabilities

Amazon Neptune ML for machine learning on graphs

There is often valuable information in large connected datasets that can be hard to extract using queries based on human intuition alone. Machine learning (ML) techniques can help find hidden correlations in graphs with billions of relationships. These correlations can be helpful for recommending products, predicting credit worthiness, identifying fraud, and many other things.

The Neptune ML feature makes it possible to build and train useful machine learning models on large graphs in hours instead of weeks. To accomplish this, Neptune ML uses graph neural network (GNN) technology powered by Amazon SageMaker and the Deep Graph Library (DGL) (which is open-source). Graph neural networks are an emerging field in artificial intelligence (see, for example, A Comprehensive Survey on Graph Neural Networks). For a hands-on tutorial about using GNNs with DGL, see Learning graph neural networks with Deep Graph Library.

Note

Graph vertices are identified in Neptune ML models as "nodes". For example, vertex classification uses a node-classification machine learning model, and vertex regression uses a node-regression model.

What Neptune ML can do

Neptune supports both transductive inference, which returns predictions that were pre-computed at the time of training, based on your graph data at that time, and inductive inference, which returns applies data processing and model evaluation in real time, based on current data. See The difference between inductive and transductive inference.

Neptune ML can train machine learning models to support five different categories of inference:

Types of inference task currently supported by Neptune ML

  • Node classification   –   predicting the categorical feature of a vertex property.

    For example, given the movie The Shawshank Redemption, Neptune ML can predict its genre property as story from a candidate set of [story, crime, action, fantasy, drama, family, ...].

    There are two types of node-classification tasks:

    • Single-class classification: In this kind of task, each node has only one target feature. For example, the property, Place_of_birth of Alan Turing has the value UK.

    • Multi-class classification: In this kind of task, each node can have more than one target feature. For example, the property genre of the film The Godfather has the values crime and story.

  • Node regression   –   predicting a numerical property of a vertex.

    For example, given the movie Avengers: Endgame, Neptune ML can predict that its property popularity has a value of 5.0.

  • Edge classification   –   predicting the categorical feature of an edge property.

    There are two types of edge-classification tasks:

    • Single-class classification: In this kind of task, each edge has only one target feature. For example, a ratings edge between a user and a movie might have the property, liked, with a value of either "Yes" or "No".

    • Multi-class classification: In this kind of task, each edge can have more than one target feature. For example, a ratings between a user and movie might have multiple values for the property tag such as "Funny", "Heartwarming", "Chilling", and so on.

  • Edge regression   –   predicting a numerical property of an edge.

    For example, a rating edge between a user and a movie might have the numerical property, score, for which Neptune ML could predict a value given a user and a movie.

  • Link prediction   –   predicting the most likely destination nodes for a particular source node and outgoing edge, or the most likely source nodes for a given destination node and incoming edge.

    For example, with a drug-disease knowledge graph, given Aspirin as the source node, and treats as the outgoing edge, Neptune ML can predict the most relevant destination nodes as heart disease, fever, and so on.

    Or, with the Wikimedia knowledge graph, given President-of as the edge or relation and United-States as the destination node, Neptune ML can predict the most relevant heads as George Washington, Abraham Lincoln, Franklin D. Roosevelt, and so on.

Note

Node classification and Edge classification only support string values. That means that numerical property values such as 0 or 1 are not supported, although the string equivalents "0" and "1" are. Similarly, the Boolean property values true and false don't work, but "true" and "false" do.

With Neptune ML, you can use machine learning models that fall in two general categories:

Types of machine learning model currently supported by Neptune ML

  • Graph Neural Network (GNN) models   –   These include Relational Graph Convolutional Networks (R-GCNs). GNN models work for all three types of task above.

  • Knowledge-Graph Embedding (KGE) models   –   These include TransE, DistMult, and RotatE models. They only work for link prediction.

User defined models   –   Neptune ML also lets you provide your own custom model implementation for all the types of tasks listed above. You can use the Neptune ML toolkit to develop and test your python-based custom model implementation before using the Neptune ML training API with your model. See Custom models in Neptune ML for details about how to structure and organize your implementation so that it's compatible with Neptune ML's training infrastructure.