# GDS (Neo4j Graph Data Science)

[[GDS Algorithms]]

## variants

  - Named graph variant
    - The graph to operate over will be read from the graph catalog.
  - Anonymous graph variant
    - The graph to operate over will be created and deleted as part of the algorithm execution

Each syntax variant additionally provides different execution modes. These are the supported execution modes:

  - stream
    - Returns the results of the algorithm as a stream of records
  - stats
    - Returns a single record of summary statistics, but does not write to the Neo4j database
  - mutate
    - Writes the results of the algorithm to the in-memory graph and returns a single record of summary statistics. This mode is designed for the named graph variant, as its effects will be invisible on an anonymous graph.
  - write
    - Writes the results of the algorithm to the Neo4j database and returns a single record of summary statistics.


## Centrality algorithms

  - Production-quality
    - [[Page Rank]]
    - [[Article Rank]]
    - [[Eigenvector Centrality]]
    - [[Betweenness Centrality]]
    - [[Degree Centrality]]
  - Alpha
    - [[Closeness Centrality]]
    - [[Harmonic Centrality]]
    - [[HITS]]
    - [[Influence Maximization]]

## Community detection algorithms

Community detection algorithms are used to evaluate how groups of nodes are clustered or partitioned, as well as their tendency to strengthen or break apart.

  - Production-quality
    - [[Louvain]]
    - [[Label Propagation]]
    - [[Weakly Connected Components]]
    - [[Triangle Count]]
    - [[Local Clustering Coefficient]]


## Similarity algorithms

Similarity algorithms compute the similarity of pairs of nodes using different vector-based metrics.

  - Production-quality
    - [[Node Similarity]]
  - Beta
    - [[K-Nearest Neighbors]]
  - Alpha
    - [[Approximate Nearest Neighbors]]
    - [[Cosine Similarity]]
    - [[Euclidean Similarity]]
    - [[Jaccard Similarity]]
    - [[Overlap Similarity]]
    - [[Pearson Similarity]]

## Path finding algorithms

  - Production-quality
    - [[Dijkstra Source-Target]]
    - [[Dijkstra Single-Source]]
    - [[A*]]
    - [[Yen's algorithm]]

## Link Prediction algorithms

  - Alpha
    - [[Adamic Adar]]
    - [[Common Neighbors]]
    - [[Preferential Attachment]]
    - [[Resource Allocation]]
    - [[Same Community]]
    - [[Total Neighbors]]

## Node embeddings

Node embedding algorithms compute low-dimensional vector representations of nodes in a graph. These vector, also called embeddings, can be used for machine learning.

  - Production-quality
    - [[FastRP]]
  - Beta
    - [[GraphSAGE]]
    - [[Node2Vec]]

## Machine Learning Models

The machine learning procedures in Neo4j [[GDS]] allow you to train supervised machine learning models. Models can then be accessed via the [[Model Catalog]] and used to make predictions about your graph.

To help with working with ML models, these are additional guides for pre-processing and hyperparameter tuning available in:

  - [Pre-processing](https://neo4j.com/docs/graph-data-science/current/algorithms/ml-models/pre-processing/)
  - [Tuning parameters for training](https://neo4j.com/docs/graph-data-science/current/algorithms/ml-models/models-tuning/)

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  - Alpha
    - [[Node Classification]]
    - [[Link Prediction]]


## 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]]

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- [[Pregel API]]