Recipes

Visual Graph provides recipes to build graph databases, query them, compute graph algorithms, and prepare graph-related datasets.

• Build Graph Recipe
• Execute Cypher Recipe
• Projected Graph Recipe
• Graph Features Recipe
• Graph Clustering Recipe
• Collect Nodes Recipe
• Collect Edges Recipe

Deprecated Compute PageRank recipe

The standalone Compute PageRank recipe is deprecated and kept for compatibility. For new flows, use the Graph features recipe and select the PageRank algorithm.

Graph database recipe settings

Recipes that run on a graph database can work with a graph database published by Visual Graph to a Dataiku Folder.

For Neo4j, these recipes can also target an unmanaged database directly by leaving the graph folder empty and selecting Neo4j (unmanaged), the Neo4j connection, and the database name in the recipe settings.

Common graph database settings are:

Graph folder

Optional Dataiku Folder that contains your materialized graph database. Leave it empty to run on an unmanaged Neo4j database directly.

Database type

If you provided a graph database folder, the database type is detected from that folder. Otherwise, select Neo4j (unmanaged).

Neo4j connection

If you selected a Neo4j database type, select the Neo4j connection to use.

Database name

If you are running on an unmanaged Neo4j database directly, select the database name to use.

Algorithm execution and sampling

Some Visual Graph recipes let you choose where the algorithm runs:

• In database runs the algorithm directly in the graph database when the selected backend supports it.

• DSS loads the selected graph into DSS backend Python memory and runs the algorithm there.

The Graph features and Graph clustering recipes currently run in DSS. The deprecated standalone Compute PageRank recipe can run either In database or in DSS.

When sampling is disabled, DSS execution loads the full selected graph into backend memory. When sampling is enabled, the recipe runs on a limited subgraph. Sampling is useful for exploration on large graphs, but the resulting values are computed on the sample, not on the full graph.

Unsupported execution engine and sampling combinations are rejected before the algorithm starts. If a selected backend or algorithm does not support a requested mode, the job fails with an explicit error instead of silently switching to another mode.

Common execution settings are:

Execution engine

Choose where the algorithm runs.

Enable sampling

Enable this option to compute on a sampled subgraph instead of the full selected graph.

Sampling mode

Choose how to build the subgraph processed by the recipe. The current sampling mode loads a bounded number of nodes and edges.

Sample limit per group

Set the maximum number of nodes loaded per selected node group and relationships loaded per selected edge group.