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Neo4j Graph Data Science
Product Version
    • The Neo4j Graph Data Science Library Manual v2.13
    • Introduction
    • Installation
      • Supported Neo4j versions
      • Neo4j Desktop
      • Neo4j Server
      • Neo4j on Docker
      • GDS Enterprise Edition
      • Configure Apache Arrow server
      • System Requirements
      • GDS Sessions
    • Getting started
      • Basic workflow
      • End-to-end workflow
      • Machine learning pipeline
    • Common usage
      • Memory Estimation
      • Projecting graphs
      • Running algorithms
      • Logging
      • Monitoring system
      • System Information
    • Graph management
      • Creating graphs
        • Cypher projection
        • Native projection
        • Apache Arrow projection
        • Filtering
        • Sampling
          • Random walk with restarts sampling
          • Common Neighbour Aware Random Walk sampling
        • Random generation
        • Cypher projection (deprecated)
      • Catalog operations
        • Listing graphs
        • Check if a graph exists
        • Dropping graphs
      • Reading from graphs
        • Streaming nodes
        • Streaming relationships
      • Updating graphs
        • Adding node labels
        • Converting directed relationships to undirected
        • Collapse Path
        • Dropping parts of the graph
      • Writing back to Neo4j
        • Writing node properties and labels
        • Writing relationships
      • Exporting graphs
        • Export to a new Neo4j database
        • Export to CSV
        • Export using Apache Arrow
      • Utility functions
      • Administration
        • Access control
        • Backup and restore
    • Graph algorithms
      • Syntax overview
      • Centrality
        • Article Rank
        • Articulation Points
        • Betweenness Centrality
        • Bridges
        • CELF
        • Closeness Centrality
        • Degree Centrality
        • Eigenvector Centrality
        • PageRank
        • Harmonic Centrality
        • HITS
      • Community detection
        • Conductance metric
        • K-Core Decomposition
        • K-1 Coloring
        • K-Means Clustering
        • Label Propagation
        • Leiden
        • Local Clustering Coefficient
        • Louvain
        • Modularity metric
        • Modularity Optimization
        • Strongly Connected Components
        • Triangle Count
        • Weakly Connected Components
        • Approximate Maximum k-cut
        • Speaker-Listener Label Propagation
      • Similarity
        • Node Similarity
        • Filtered Node Similarity
        • K-Nearest Neighbors
        • Filtered K-Nearest Neighbors
        • Similarity functions
      • Path finding
        • Delta-Stepping Single-Source Shortest Path
        • Dijkstra Source-Target Shortest Path
        • Dijkstra Single-Source Shortest Path
        • A* Shortest Path
        • Yen’s Shortest Path algorithm
        • Minimum Weight Spanning Tree
        • Minimum Weight k-Spanning Tree
        • Minimum Directed Steiner Tree
        • Minimum Directed Steiner Tree
        • All Pairs Shortest Path
        • Random Walk
        • Breadth First Search
        • Depth First Search
        • Bellman-Ford Single-Source Shortest Path
        • Longest Path for DAG
      • DAG algorithms
        • Topological Sort
        • Longest Path for DAG
      • Node embeddings
        • Fast Random Projection
        • GraphSAGE
        • Node2Vec
        • HashGNN
      • Topological link prediction
        • Adamic Adar
        • Common Neighbors
        • Preferential Attachment
        • Resource Allocation
        • Same Community
        • Total Neighbors
      • Pregel API
    • Machine learning
      • Pre-processing
        • Scale Properties
        • One Hot Encoding
        • Split Relationships
      • Node embeddings
        • Fast Random Projection
        • GraphSAGE
        • Node2Vec
        • HashGNN
      • Node property prediction
        • Node classification pipelines
          • Configuring the pipeline
          • Training the pipeline
          • Applying a trained model for prediction
        • Node regression pipelines
          • Configuring the pipeline
          • Training the pipeline
          • Applying a trained model for prediction
      • Link prediction pipelines
        • Configuring the pipeline
        • Training the pipeline
        • Applying a trained model for prediction
        • Theoretical considerations
      • Pipeline catalog
        • Listing pipelines
        • Checking if a pipeline exists
        • Removing pipelines
      • Model catalog
        • Listing models
        • Checking if a model exists
        • Dropping models
        • Storing models on disk
        • Publishing models
      • Training methods
        • Logistic regression
        • Random forest
        • Multilayer Perceptron
        • Linear regression
      • Auto-tuning
    • Production deployment
      • Defaults and Limits
      • Transaction Handling
      • Using GDS and composite databases (formerly known as Fabric)
      • GDS with Neo4j cluster
      • GDS Configuration Settings
      • GDS Feature Toggles
    • Python client
    • Bloom visualization
    • Appendix
      • Operations reference
        • Graph Catalog
        • Graph Algorithms
        • Machine Learning
        • Additional Operations
        • Configuration Settings
      • Migration from Graph Data Science library Version 1.x
        • Common changes
        • Graph projection
        • Graph listing
        • Graph drop
        • Memory estimation
        • Algorithms
        • Machine Learning
      • Migration from Legacy to new Cypher projection
      • Migration from Alpha Cypher Aggregation to new Cypher projection

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Node property prediction

Node property prediction pipelines provide an end-to-end workflow for predicting either discrete labels or numerical values for nodes with supervised machine learning. The Neo4j Graph Data Science library support the following node property prediction pipelines:

  • Beta

    • Node classification pipelines

  • Alpha

    • Node regression pipelines

HashGNN Node classification pipelines

Graph Data Science courses on GraphAcademy

Follow our Graph Data Analytics learning path to learn how to apply graph thinking to your machine learning pipelines.

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