Writing node properties and labels

It is possible to persist computed node properties into a Neo4j database. We refer to this operation as writing. This is similar to what an algorithm write execution mode does, but allows more fine-grained control over the operations.

Syntax

Syntax descriptions of the different operations over node properties

CALL gds.graph.nodeProperties.write(
    graphName: String,
    nodeProperties: String or Map of Strings or List of Strings and/or Maps of Strings,
    nodeLabels: String or List of Strings,
    configuration: Map
)
YIELD
    writeMillis: Integer,
    propertiesWritten: Integer,
    graphName: String,
    nodeProperties: String or List of String

Table 1. Parameters
Name	Type	Optional	Description
graphName	String	no	The name under which the graph is stored in the catalog.
nodeProperties	String, Map of Strings, or List of Strings and/or Maps of Strings	no	The node properties in the graph to write back. To write back properties with new custom names, store them as entries inside maps in the form: `nodeProperty: 'renamedProperty'`.
nodeLabels	String or List of Strings	yes	The node labels to write back their node properties.
configuration	Map	yes	Additional parameters to configure writeNodeProperties.

Table 2. Configuration
Name	Type	Default	Description
concurrency	Integer	4	The number of concurrent threads used for running the procedure. Also provides the default value for `writeConcurrency`
writeConcurrency	Integer	'concurrency'	The number of concurrent threads used for writing the node properties.

Table 3. Results
Name	Type	Description
writeMillis	Integer	Milliseconds for writing result data back to Neo4j.
propertiesWritten	Integer	Number of properties written.
graphName	String	The name of a graph stored in the catalog.
nodeProperties	String or List of String	The written node properties.

CALL gds.graph.nodeLabel.write(
    graphName: String,
    nodeLabel: String,
    configuration: Map
)
YIELD
    writeMillis: Integer,
    nodeLabelsWritten: Integer,
    nodeLabel: String,
    graphName: String,
    nodeCount: Integer,
    configuration: Map

Table 4. Parameters
Name	Type	Optional	Description
graphName	String	no	The name under which the graph is stored in the catalog.
nodeLabel	String	no	The node label to write back.
configuration	Map	yes	Additional parameters to configure writeNodeProperties.

Table 5. Configuration
Name	Type	Default	Description
nodeFilter	String	n/a	A Cypher predicate for filtering nodes in the input graph. See Projecting a subgraph.
concurrency	Integer	4	The number of concurrent threads used for running the procedure.

Table 6. Results
Name	Type	Description
writeMillis	Integer	Milliseconds for writing result data back to Neo4j.
nodeLabelsWritten	Integer	Number of node labels written.
graphName	String	The name of a graph stored in the catalog.
nodeLabel	String	The written node label.
nodeCount	Integer	The total number of nodes in the graph.
configuration	Map	The configuration used to run the procedure.

Examples

All the examples below should be run in an empty database.

In order to demonstrate the GDS capabilities over node properties, we are going to create a small social network graph in Neo4j and project it into our graph catalog.

The following Cypher statement will create the example graph in the Neo4j database:

CREATE
  (florentin:Person { name: 'Florentin', age: 16 }),
  (adam:Person { name: 'Adam', age: 18 }),
  (veselin:Person { name: 'Veselin', age: 20 }),
  (hobbit:Book { name: 'The Hobbit', numberOfPages: 310 }),
  (florentin)-[:KNOWS { since: 2010 }]->(adam),
  (florentin)-[:KNOWS { since: 2018 }]->(veselin),
  (adam)-[:READ]->(hobbit)

Project the small social network graph:

CALL gds.graph.project(
  'socialGraph',
  {
    Person: {properties: "age"},
    Book: {}
  },
  ['KNOWS', 'READ']
)

Compute the Degree Centrality in our social graph:

CALL gds.degree.mutate('socialGraph', {mutateProperty: 'score'})

Write node properties

To write the 'score' property for all node labels in the social graph, we use the following query:

Write the score property back to Neo4j:

CALL gds.graph.nodeProperties.write('socialGraph', ['score'])
YIELD propertiesWritten

Table 7. Results
propertiesWritten
4

The above example requires the score property to be present on at least one projected node label, and the properties will be written for all such labels.

Renaming properties when writing back

It is possible to rename node properties and write them back to the database under a custom name. For this, you can use a map where each entry is a tuple {nodeProperty: 'renamedProperty'} i.e., the key corresponds to an existing node property in the in-memory graph and the value corresponds to the name to be written back to the database.

For convenience, a map may hold more than one entries. The nodeProperties configuration parameter accepts both strings and maps and any combination of the two inside a list. This can be helpful when we only want to rename a handful of properties.

Write the age and score properties back to Neo4j with a new name for score:

CALL gds.graph.nodeProperties.write('socialGraph', ['age', {score: 'writtenScore'}])
YIELD nodeProperties

Table 8. Results
nodeProperties
[age, writtenScore]

In the above example, we write age back to the database with its default name whereas we renamed score to writtenScore by using a map.

NodeLabels

The procedure can be configured to write just the properties for some specific node labels. In the following example, we will only write back the scores of the Person nodes.

Write node properties of a specific projected node label to Neo4j:

CALL gds.graph.nodeProperties.write('socialGraph', ['score'], ['Person'])
YIELD propertiesWritten

Table 9. Results
propertiesWritten
3

If the nodeLabels parameter is specified, it is required that all given node labels have all of the given properties.

Write Node Label

To write a new node label to the database for nodes with score higher than 0, we use the following query:

Write the Reader node label back to Neo4j:

CALL gds.graph.nodeLabel.write('socialGraph', 'Reader', { nodeFilter: 'n.score > 0.0' })
YIELD graphName, nodeCount, nodeLabel, nodeLabelsWritten

Table 10. Results
graphName	nodeCount	nodeLabel	nodeLabelsWritten
"socialGraph"	4	"Reader"	2

Query the Reader node label:

MATCH (n:Reader) RETURN n.name AS name, labels(n) AS labels
ORDER BY name ASC

Table 11. Results
name	labels
"Adam"	[Person, Reader]
"Florentin"	[Person, Reader]

As we can see from the database Veselin who has score: 0.0 is not a Reader.