PageRank

Introduction

PageRank is an algorithm used to measure the relative importance of nodes in a graph based on their connections.

Output

The algorithm produces the following fields:

Field Name	Description
id	ID of the vertex (node)
score	The PageRank score of the vertex (node)

Using Cypher

Query Example

CALL algo.paral.pagerank({
    labels: ['Page'],
    relationshipTypes: ['LINKS'],
    relationshipWeightProperty: 'weight',
    maxIterations: 20,
    dampingFactor: 0.85
    }) YIELD id, score 
RETURN id, score 

Parameters

Name	Description	Required
labels	Specifies the node labels to include in the algorithm	Yes
relationshipTypes	Specifies the relationship types to include	Yes
relationshipWeightProperty	Defines the property name representing edge weights	No
maxIterations	Maximum number of iterations, default is 30	No
dampingFactor	Factor representing the probability of continuing along links, default is 0.85	No

Exporting Query Results

For large datasets, export the results to object storage:

EXPORT TO 's3://my_bucket/target_dir'
PROPERTIES {
  catalog: 'puppygraph_catalog_name'
}
CALL algo.paral.pagerank({
    labels: ['Page'],
    relationshipTypes: ['LINKS'],
    relationshipWeightProperty: 'weight',
    }) YIELD id, score 
RETURN id, score 

Using Gremlin

Query Example

graph.compute().program(
  PageRankProgram.build()
    .maxIteration(20)
    .vertices("Page")
    .edges("LINKS")
    .edgeProperties("amount")
    .setParam("weight", "weight", "threshold", 10.0, "dampingFactor", 0.7)
    .create()
).submit().get().toList()

Parameters

Method	Description
maxIteration(int n)	Sets the maximum number of iterations, default is 30
vertices(String... names)	Specifies the vertex labels to include
edges(String... names)	Specifies the edge labels to include
edgeProperties(String... names)	Sets the property name for edge weights
setParams(Object... args)	Adds additional parameters using alternating keys and values

Additional Parameters

Key	Description	Default Value
dampingFactor	Factor representing the probability of continuing along links	0.85
weight	Specifies the edge weight property or a constant value as weight	1.0
threshold	Minimum edge weight value for it to be included in the algorithm	0.0

Exporting Query Results

For large datasets, use the save() method to export the results.

graph.compute().program(
    PageRankProgram.build()
            .maxIteration(20)
            .vertices("Page")
            .edges("LINKS")
            .edgeProperties("amount")
            .setParam("weight", "weight", "threshold", 10.0)
            .create()
).submit().get().save([
  "exportTo": "s3://my_bucket/target_dir",
  "catalog": "puppygraph_catalog_name"
])