Neo4j Graph Database Fundamentals · Lesson

GDS Pipelines and Machine Learning

Discover how to build and manage graph data science pipelines within GDS, integrating with machine learning workflows.

GDS Pipelines and Machine Learning is a free Neo4j Graph Database Fundamentals lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Neo4j Graph Database Fundamentals learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What are GDS Pipelines?

Welcome to GDS Pipelines! In the Neo4j Graph Data Science (GDS) library, a pipeline is a structured workflow for common graph data science tasks.

Think of it as a blueprint that defines a sequence of steps, from feature engineering using graph algorithms to training and deploying machine learning models.

Why Use GDS Pipelines?

GDS Pipelines offer several key benefits:

Reproducibility: Define your entire workflow once and reuse it.
Automation: Streamline complex tasks involving multiple graph algorithms and ML steps.
Operationalization: Easily deploy graph-based machine learning models for continuous prediction.

They help bridge the gap between experimentation and production.

Creating a Prediction Pipeline

One common pipeline type is the Node Property Prediction Pipeline. This helps predict a specific property on nodes based on other node features and graph structure.

Let's create an empty pipeline named 'myChurnPrediction':

CALL gds.pipeline.nodePropertyPrediction.create('myChurnPrediction')
YIELD pipeline
RETURN pipeline.name AS name, pipeline.type AS type

Projecting Your Graph Data

Before using a pipeline, you need to project your graph into GDS memory. This makes nodes and relationships accessible for algorithms and features.

Here, we project a simple 'User' graph with 'KNOWS' relationships:

CALL gds.graph.project(
    'mySocialGraph',
    'User',
    { KNOWS: { orientation: 'UNDIRECTED' } }
)
YIELD graphName, nodeCount, relationshipCount
RETURN graphName, nodeCount, relationshipCount

Adding Node Property Features

Pipelines allow you to specify which existing node properties should be used as features for your machine learning model. These are direct attributes of your nodes.

Let's add 'age' and 'income' properties as features to our pipeline:

CALL gds.pipeline.nodePropertyPrediction.addNodePropertySteps('myChurnPrediction', {
    nodeProperties: ['age', 'income']
})
YIELD pipeline
RETURN pipeline.name, pipeline.nodePropertySteps

Integrating Graph Algorithm Features

The real power of GDS pipelines is integrating graph algorithm results as features. These capture structural insights that simple node properties can't.

We can add PageRank scores as a feature to our pipeline:

CALL gds.pipeline.nodePropertyPrediction.addPageRank('myChurnPrediction', {
    maxIterations: 10,
    dampingFactor: 0.85,
    mutateProperty: 'pageRankScore' // This becomes a feature
})
YIELD pipeline
RETURN pipeline.name, pipeline.pageRankSteps

Configuring the Machine Learning Model

After defining your features, you specify the machine learning model that will perform the prediction. GDS supports various models like Logistic Regression, Random Forest, and GNNs.

Let's add a Logistic Regression model to predict 'isChurned':

CALL gds.pipeline.nodePropertyPrediction.addLogisticRegression('myChurnPrediction', {
    targetProperty: 'isChurned', // The node property we want to predict
    maxIterations: 100,
    penalty: 'l2'
})
YIELD pipeline
RETURN pipeline.name, pipeline.logisticRegressionSteps

Training Your GDS Pipeline

With features and a model defined, you can now train the pipeline. This executes all feature generation steps, then trains the specified ML model on the generated features.

We'll train our pipeline on 'mySocialGraph' and name the resulting model 'churnPredictionModel':

CALL gds.pipeline.nodePropertyPrediction.train('myChurnPrediction', {
    modelName: 'churnPredictionModel',
    graphName: 'mySocialGraph',
    nodeLabels: ['User'],
    randomSeed: 42
})
YIELD modelInfo
RETURN modelInfo.modelName AS modelName, modelInfo.trainingMetrics.f1Score.weighted AS f1Score

Making Predictions with the Model

Once trained, your model (which is part of the pipeline) can be used to generate predictions on your graph data. You can either stream results or mutate the graph with new properties.

Let's stream predictions for our 'churnPredictionModel':

CALL gds.pipeline.nodePropertyPrediction.predict.stream('churnPredictionModel', {
    graphName: 'mySocialGraph',
    nodeLabels: ['User'],
    topN: 1 // Get the top predicted class
})
YIELD nodeId, predictedProperty, probability
RETURN gds.util.asNode(nodeId).name AS user, predictedProperty, probability
LIMIT 5

Managing Your Pipelines

You can list all active pipelines and their configurations using gds.pipeline.list(). When a pipeline is no longer needed, you can remove it with gds.pipeline.drop().

Let's see the pipelines we currently have:

CALL gds.pipeline.list()
YIELD name, type, creationTime
RETURN name, type, creationTime

GDS Pipeline Components

A GDS pipeline combines various steps to create a complete data science workflow. Which of the following are valid components or steps you can add to a GDS Node Property Prediction Pipeline?

Recap: Pipelines for ML

Great job! You've learned how GDS Pipelines provide a structured and reproducible way to integrate graph algorithms with machine learning workflows.

Pipelines define a sequence of steps.
They combine feature engineering (from existing properties and graph algorithms) with ML model training.
They enable efficient prediction and operationalization of graph-based insights.

Keep exploring GDS to unlock more advanced graph analytics!

Frequently asked questions

Is the “GDS Pipelines and Machine Learning” lesson free?

Yes — the full text of “GDS Pipelines and Machine Learning” is free to read here on the web, and the Neo4j Graph Database Fundamentals course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Neo4j Graph Database Fundamentals course, upgrade to CoddyKit PRO.

What will I learn in “GDS Pipelines and Machine Learning”?

Discover how to build and manage graph data science pipelines within GDS, integrating with machine learning workflows. You practise Neo4j Graph Database Fundamentals with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Neo4j Graph Database Fundamentals?

No prior experience is required. Neo4j Graph Database Fundamentals on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “GDS Pipelines and Machine Learning” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Neo4j Graph Database Fundamentals lesson?

Yes. Every Neo4j Graph Database Fundamentals lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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