GraphML Quick Start

Predict missing labels and uncover hidden patterns in your graph with machine learning, no data science pipeline required

Prerequisites

Access to the platform with GraphML services enabled.
A database with graph data (node and edge collections).
For Node Classification: some labeled nodes for training. For Node Embeddings: no labels required.

Train a model and predict

Create a project

In the Arango Contextual Data Platform, go to Agentic AI Suite > GraphML and click Add new project.

Featurize your graph

Select your graph, choose the node collections and attributes to use as features, configure the job, and click Begin featurization.

Train a model

Choose the task type - Node Classification or Node Embeddings - set the parameters, and click Begin training.

Select the best model

Review the training metrics, pick the best-performing model, and click Select model for prediction.

Run a prediction

Configure the prediction job (optionally enable scheduling) and click Run Prediction.

Enable ArangoML

arangoml = %enable_arangoml
project = arangoml.get_or_create_project("my_project")

Featurize

job = arangoml.jobs.featurize(featurization_spec)
arangoml.wait_for_featurization(job.job_id)

Train

job = arangoml.jobs.train(training_spec)
arangoml.wait_for_training(job.job_id)

Select the best model

model = arangoml.get_best_model(
    "my_project", training_job_id, sort_parent_key, sort_child_key
)

Predict

job = arangoml.jobs.predict(prediction_spec)
arangoml.wait_for_prediction(job.job_id)

You now have predicted labels (Node Classification) or vector embeddings (Node Embeddings) written to your target collection, ready for clustering, similarity search, anomaly detection, or link prediction.

Next steps

Setup: The full walkthrough.
UI: The web interface in detail.
Notebooks API: All arangoml methods and specs.