Algorithms¶

Graph algorithms are a subset of tools for graph analytics. We are typically looking for global patterns and structures.

The algorithms will help to make sense of connected data. Graph algorithms are particularly suited to understand datasets that are highly connected.

Broadly speaking, there are six different kind of algorithms to be applied on networks:

Pathfinding Pathfinding is closely related to the shortest path problem, within graph theory, which examines how to identify the path that best meets some criteria (shortest, cheapest, fastest, etc) between two points in a large network.
Centrality Measures Centrality algorithms are used to understand the roles of particular nodes in a graph and their impact on that network
Community Detections Community formation is common in all types of networks, and identifying them is essential for evaluating group behavior and emergent phenomena. The general principle in finding communities is that its members will have more relationships within the group than with nodes outside their group. Identifying these related sets reveals clusters of nodes, isolated groups, and network structure.
Similarity in order calculate the similarity of nodes.
Link Prediction these algorithms determine the closeness of pairs of nodes.
Node Embeddings these algorithms compute vector representations of nodes in a graph.

In Neo4j algorithms exist in one of three tiers of maturity:

Note

Three Tiers

Production-quality Indicates that the algorithm has been tested with regards to stability and scalability. Algorithms in this tier are prefixed with gds.<algorithm> and are supported by Neo4j.
Beta Indicates that the algorithm is a candidate for the production-quality tier. Algorithms in this tier are prefixed with gds.beta.<algorithm>.
Alpha Indicates that the algorithm is experimental and might be changed or removed at any time. Algorithms in this tier are prefixed with gds.alpha.<algorithm>.

Detailed Overview:

Last change: Oct 30, 2023

Graphs