Neo4j Graph Algorithms Release 3.2.9.0 - More Huge Graphs and Graph Loading

Thanks a lot to Paul Horn and Martin Knobloch from Avantgarde Labs for their tireless work on the graph algorithms. Thanks also to Tomasz Bratanic for the great documentation work.

New algorithms

• Dangalchev closeness centrality
• Harmonic closeness centrality
• Approximate Betweenness centrality
• K spanning tree

More support for large Graphs

In the previous release we added support for large graphs with the graph:huge config parameter.
This release adds huge graph support to the following algorithms:

• Closeness Centrality
• Harmonic Centrality
• Union Find
• Strongly Connected Components
• Triangle Counting
• Louvain

Graph Loading

It can take some time to load large graphs into the algorithm data structures, so you can now pre-load graphs and then later refer to them by name when calling the graph algorithms.
After usage they can be removed from memory to free resources used.

Learn more in the README

Documentation

You can find documentation for the new algorithms and examples of how to apply them on the Yelp business dataset.

The new algorithms have also been demonstrated in several places:

• Our Data science training course - run :play data_science in the Neo4j browser
• On Tomasz' Blog
• In our recent webinar

Installation

1. Download the jar into $NEO4J_HOME/plugins
2. Add dbms.security.procedures.unrestricted=algo.* to your $NEO4J_HOME/conf/neo4j.conf
3. Restart Neo4j