This chapter provides a brief introduction of the main concepts in the Neo4j Graph Data Science library.
This library provides efficiently implemented, parallel versions of common graph algorithms for Neo4j, exposed as Cypher procedures.
Graph algorithms are used to compute metrics for graphs, nodes, or relationships.
They can provide insights on relevant entities in the graph (centralities, ranking), or inherent structures like communities (community-detection, graph-partitioning, clustering).
Many graph algorithms are iterative approaches that frequently traverse the graph for the computation using random walks, breadth-first or depth-first searches, or pattern matching.
Due to the exponential growth of possible paths with increasing distance, many of the approaches also have high algorithmic complexity.
Fortunately, optimized algorithms exist that utilize certain structures of the graph, memoize already explored parts, and parallelize operations. Whenever possible, we’ve applied these optimizations.
The Neo4j Graph Data Science library contains a large number of algorithms, which are detailed in the Algorithms chapter.
Algorithms in GDS have specific ways to make use of various aspects of its input graph(s). We call these algorithm traits. When an algorithm supports an algorithm trait this indicates that the algorithm has been implemented to produce well-defined results in accordance with the trait. The following algorithm traits exist:
The algorithm is well-defined on a directed graph.
The algorithm is well-defined on an undirected graph.
The algorithm will treat all nodes and relationships in its input graph(s) similarly, as if they were all of the same type. If multiple types of nodes or relationships exist in the graph, this must be taken into account when analysing the results of the algorithm.
The algorithm has the ability to distinguish between nodes and/or relationships of different types.
The algorithm supports configuration to set node and/or relationship properties to use as weights. These values can represent cost, time, capacity or some other domain-specific property, specified via the nodeWeightProperty and relationshipWeightProperty configuration parameters. The algorithm will by default consider each node and/or relationship as equally important.
In order to run the algorithms as efficiently as possible, the Neo4j Graph Data Science library uses a specialized in-memory graph format to represent the graph data. It is therefore necessary to load the graph data from the Neo4j database into an in memory graph catalog. The amount of data loaded can be controlled by so called graph projections, which also allow, for example, filtering on node labels and relationship types, among other options.
For more information see Graph Management.
The Neo4j Graph Data Science library is available in two editions.
The open source Community Edition includes all algorithms and features, but is limited to four CPU cores.
The Neo4j Graph Data Science library Enterprise Edition:
Can run on an unlimited amount of CPU cores.
Supports the role-based access control system (RBAC) from Neo4j Enterprise Edition.
Supports various additional model catalog features
Supports an optimized in-memory graph implementation
For more information see System Requirements - CPU.
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