17 Using the Machine Learning Library (PgxML) for Graphs

The graph server (PGX) provides a machine learning library oracle.pgx.api.mllib, which supports graph-empowered machine learning algorithms.

The following machine learning algorithms are currently supported:

• Using the DeepWalk Algorithm
  DeepWalk is a widely employed vertex representation learning algorithm used in industry.
• Using the Supervised GraphWise Algorithm (Vertex Embeddings and Classification)
  Supervised GraphWise is an inductive vertex representation learning algorithm which is able to leverage vertex feature information. It can be applied to a wide variety of tasks, including vertex classification and link prediction.
• Using the Supervised EdgeWise Algorithm (Edge Embeddings and Classification)
  SupervisedEdgeWise is an inductive edge representation learning algorithm which is able to leverage vertex and edge feature information. It can be applied to a wide variety of tasks, including edge classification and link prediction.
• Using the Unsupervised GraphWise Algorithm (Vertex Embeddings)
  Unsupervised GraphWise is an unsupervised inductive vertex representation learning algorithm which is able to leverage vertex information. The learned embeddings can be used in various downstream tasks including vertex classification, vertex clustering and similar vertex search.
• Using the Unsupervised EdgeWise Algorithm
  UnsupervisedEdgeWise is an inductive edge representation learning algorithm which is able to leverage vertex and edge feature information. It can be applied to a wide variety of tasks, including unsupervised learning edge embeddings for edge classification.
• Using the Unsupervised Anomaly Detection GraphWise Algorithm (Vertex Embeddings and Anomaly Scores)
  UnsupervisedAnomalyDetectionGraphWise is an inductive vertex representation learning and anomaly detection algorithm which is able to leverage vertex and edge feature information. Although it can be applied to a wide variety of tasks, it is particularly suitable for unsupervised learning of vertex embeddings for anomaly detection. After training this model, it is possible to infer anomaly scores or labels for unseen nodes.
• Using the Pg2vec Algorithm
  Pg2vec learns representations of graphlets (partitions inside a graph) by employing edges as the principal learning units and thereby packing more information in each learning unit (as compared to employing vertices as learning units) for the representation learning task.
• Model Repository and Model Stores
  A model store can be used to persist the trained graph server (PGX) machine learning models along with a model name (a unique identifier of the model in a particular model store) and a description.

See Also:

Model Repository and Model Stores for information on model store management and how models can be persisted in a model store.