Neo4j Graph Database Fundamentals · Lección

Pipelines de GDS y machine learning

Descubra cómo crear y gestionar pipelines de ciencia de datos de grafos en GDS e integrarlos con flujos de trabajo de machine learning.

Pipelines de GDS y machine learning es una lección gratuita de Neo4j Graph Database Fundamentals en CoddyKit. Esta es la lección 3 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Neo4j Graph Database Fundamentals, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Neo4j Graph Database Fundamentals incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

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!

Preguntas frecuentes

¿La lección «Pipelines de GDS y machine learning» es gratis?

Sí — el texto completo de «Pipelines de GDS y machine learning» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Neo4j Graph Database Fundamentals, actualiza a CoddyKit PRO. El curso de Neo4j Graph Database Fundamentals incluye 4 lecciones en total.

¿Qué aprenderé en «Pipelines de GDS y machine learning»?

Descubra cómo crear y gestionar pipelines de ciencia de datos de grafos en GDS e integrarlos con flujos de trabajo de machine learning. Practicas Neo4j Graph Database Fundamentals con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar Neo4j Graph Database Fundamentals?

No se requiere experiencia previa. Neo4j Graph Database Fundamentals en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 3 de 4.

¿Cuánto tiempo toma la lección «Pipelines de GDS y machine learning»?

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¿Puedo escribir y ejecutar código en esta lección de Neo4j Graph Database Fundamentals?

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