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Neo4j Graph Data Science
    • The Neo4j Graph Data Science Library Manual v2.1
    • Introduction
    • Installation
      • Supported Neo4j versions
      • Neo4j Desktop
      • Neo4j Server
      • Enterprise Edition Configuration
      • Neo4j Docker
      • Neo4j Causal Cluster
      • Apache Arrow
      • Additional configuration options
      • System Requirements
    • Common usage
      • Memory Estimation
      • Projecting graphs
      • Running algorithms
      • Logging
      • Monitoring system
      • System Information
    • Graph management
      • Graph Catalog
        • Projecting graphs using native projections
        • Projecting graphs using Cypher
        • Projecting graphs using Cypher Aggregation
        • Projecting graphs using Apache Arrow
        • Listing graphs
        • Check if a graph exists
        • Removing graphs
        • Projecting a subgraph
        • Node operations
        • Relationship operations
        • Export operations
        • Apache Arrow operations
      • Node Properties
      • Utility functions
      • Cypher on GDS graph
      • Administration
      • Backup and Restore
    • Graph algorithms
      • Syntax overview
      • Centrality
        • PageRank
        • Article Rank
        • Eigenvector Centrality
        • Betweenness Centrality
        • Degree Centrality
        • Closeness Centrality
        • Harmonic Centrality
        • HITS
        • Influence Maximization
          • CELF
          • Greedy
      • Community detection
        • Louvain
        • Label Propagation
        • Weakly Connected Components
        • Triangle Count
        • Local Clustering Coefficient
        • K-1 Coloring
        • Modularity Optimization
        • Strongly Connected Components
        • Speaker-Listener Label Propagation
        • Approximate Maximum k-cut
        • Conductance metric
        • K-Means Clustering
        • Leiden
      • 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 algorithm Shortest Path
        • Minimum Weight Spanning Tree
        • All Pairs Shortest Path
        • Random Walk
        • Breadth First Search
        • Depth First Search
      • Node embeddings
        • Fast Random Projection
        • GraphSAGE
        • Node2Vec
      • Topological link prediction
        • Adamic Adar
        • Common Neighbors
        • Preferential Attachment
        • Resource Allocation
        • Same Community
        • Total Neighbors
      • Auxiliary procedures
        • Graph Generation
        • Collapse Path
        • Scale Properties
        • One Hot Encoding
        • Split Relationships
      • Pregel API
    • Machine learning
      • Pre-processing
      • Node embeddings
        • Fast Random Projection
        • GraphSAGE
        • Node2Vec
      • 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
        • Appendix
      • Pipeline catalog
        • Listing pipelines
        • Checking if a pipeline exists
        • Removing pipelines
      • Model catalog
        • Listing models
        • Checking if a model exists
        • Removing models
        • Storing models on disk
        • Publishing models
      • Training methods
        • Logistic regression
        • Random forest
        • Linear regression
      • Auto-tuning
    • End-to-end examples
      • FastRP and kNN example
    • Production deployment
      • Transaction Handling
      • Using GDS and Fabric
      • GDS with Neo4j Causal Cluster
      • GDS Feature Toggles
    • Python client
      • Installation
      • Getting started
      • The graph object
      • Running algorithms
      • Machine learning pipelines
      • The model object
      • Known limitations
    • Appendix
      • Operations reference
        • Graph Catalog
        • Graph Algorithms
        • Machine Learning
        • Additional Operations
      • Migration from Graph Data Science library Version 1.x
        • Common changes
        • Graph projection
        • Graph listing
        • Graph drop
        • Memory estimation
        • Algorithms
        • Machine Learning
  • Neo4j Graph Data Science
  • Graph algorithms
  • Auxiliary procedures
2.2-preview 2.1 2.0 1.8 1.1

Auxiliary procedures

Auxiliary procedures are extra tools that can be useful in your workflow.
The Neo4j GDS library includes the following auxiliary procedures, grouped by quality tier:

  • Beta

    • Graph Generation

  • Alpha

    • Collapse Path

    • Scale Properties

    • One Hot Encoding

    • Split Relationships

Total Neighbors Graph Generation

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