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Neo4j Graph Data Science
Product Version
    • The Neo4j Graph Data Science Library Manual v2.12
    • 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
        • 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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  • Neo4j Graph Data Science
  • Production deployment
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Production deployment

This chapter is divided into the following sections:

  • Defaults and Limits

  • Transaction Handling

  • Using GDS and Composite databases

  • GDS with Neo4j cluster

  • GDS Configuration Settings

  • GDS Feature Toggles

Auto-tuning Defaults and Limits

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