A projected graph can be stored in the catalog under a user-defined name. Using that name, the graph can be referred to by any algorithm in the library. This allows multiple algorithms to use the same graph without having to re-create it on each algorithm run.

There are two variants of projecting a graph from the Neo4j database into main memory:

In this section, we will give brief examples on how to create a graph using either variant. For detailed information about the configuration of each variant, we refer to the dedicated sections.

In the following two examples we show how to create a graph called my-native-graph that contains Person nodes and LIKES relationships.

Create a graph using a native projection: 

CALL gds.graph.create(
    'my-native-graph',
    'Person',
    'LIKES'
)
YIELD graphName, nodeCount, relationshipCount, createMillis;

We can also use Cypher to select the nodes and relationships to be projected into the in-memory graph.

Create a graph using a Cypher projection: 

CALL gds.graph.create.cypher(
    'my-cypher-graph',
    'MATCH (n:Person) RETURN id(n) AS id',
    'MATCH (a:Person)-[:LIKES]->(b:Person) RETURN id(a) AS source, id(b) AS target'
)
YIELD graphName, nodeCount, relationshipCount, createMillis;

After creating the graphs in the catalog, we can refer to them in algorithms by using their name.

Run Page Rank on one of our created graphs: 

CALL gds.pageRank.stream('my-native-graph') YIELD nodeId, score;

Information about graphs in the catalog can be listed using the gds.graph.list() procedure. The procedure takes an optional parameter graphName:

List information about graphs in the catalog: 

CALL gds.graph.list(
  graphName: String?
) YIELD
  graphName,
  database,
  nodeProjection,
  relationshipProjection,
  nodeQuery,
  relationshipQuery,
  nodeCount,
  relationshipCount,
  schema,
  degreeDistribution,
  density,
  creationTime,
  modificationTime,
  sizeInBytes,
  memoryUsage;

The information contains basic statistics about the graph, e.g., the node and relationship count. The result field creationTime indicates when the graph was created in memory. The result field modificationTime indicates when the graph was updated by an algorithm running in mutate mode.

The database column refers to the name of the database the corresponding graph has been created on. Referring to a named graph in a procedure is only allowed on the database it has been created on.

The schema consists of information about the nodes and relationships stored in the graph. For each node label, the schema maps the label to its property keys and their corresponding property types. Similarly, the schema maps the relationship types to their property keys and property types. The property type is either Integer, Float, List of Integer or List of Float.

The degreeDistribution field can be fairly time-consuming to compute for larger graphs. Its computation is cached per graph, so subsequent listing for the same graph will be fast. To avoid computing the degree distribution, specify a YIELD clause that omits it. Note that not specifying a YIELD clause is the same as requesting all possible return fields to be returned.

The density is the result of relationshipCount divided by the maximal number of relationships for a simple graph with the given nodeCount.

CALL gds.graph.list()
YIELD graphName, nodeCount, relationshipCount, schema;

CALL gds.graph.list('my-cypher-graph')
YIELD graphName, nodeQuery, relationshipQuery, nodeCount, relationshipCount, schema, creationTime, modificationTime, memoryUsage;

CALL gds.graph.list('my-native-graph')

CALL gds.graph.list('my-cypher-graph')
YIELD graphName, degreeDistribution;

We can check if a graph is stored in the catalog by looking up its name.

Check if a graph exists in the catalog: 

CALL gds.graph.exists('my-store-graph') YIELD exists;

We can remove node properties from a named graph in the catalog. This is useful to free up main memory or to remove accidentally created node properties.

Remove multiple node properties from a named graph: 

CALL gds.graph.removeNodeProperties('my-graph', ['pageRank', 'communityId'])

The above example requires all given properties to be present on at least one node projection, and the properties will be removed from all such projections.

The procedure can be configured to remove just the properties for some specific node projections. In the following example, we ran an algorithm on a sub-graph and subsequently remove the newly created property.

Remove node properties of a specific node projection: 

CALL gds.graph.create('my-graph', ['A', 'B'], '*')
CALL gds.wcc.mutate('my-graph', {nodeLabels: ['A'], mutateProperty: 'componentId'})
CALL gds.graph.removeNodeProperties('my-graph', ['componentId'], ['A'])

When a list of projections that are not * is specified, as in the example above, a different validation and execution is applied; It is then required that all projections have all of the given properties, and they will be removed from all of the projections.

If any of the given projections is '*', the procedure behaves like in the first example.

We can delete all relationships of a given type from a named graph in the catalog. This is useful to free up main memory or to remove accidentally created relationship types.

Delete all relationships of type T from a named graph: 

CALL gds.graph.deleteRelationships('my-graph', 'T')
YIELD graphName, relationshipType, deletedRelationships, deletedProperties

Once we have finished using the named graph we can remove it from the catalog to free up memory.

Remove a graph from the catalog: 

CALL gds.graph.drop('my-store-graph') YIELD graphName;

If we want the procedure to fail silently on non-existing graphs, we can set a boolean flag as the second parameter to false. This will yield an empty result for non-existing graphs.

Try removing a graph from the catalog: 

CALL gds.graph.drop('my-fictive-graph', false) YIELD graphName;

We can stream node properties stored in a named in-memory graph back to the user. This is useful if we ran multiple algorithms in mutate mode and want to retrieve some or all of the results. This is similar to what the stream execution mode does, but allows more fine-grained control over the operations.

Stream multiple node properties: 

CALL gds.graph.streamNodeProperties('my-graph', ['componentId', 'pageRank', 'communityId'])

The above example requires all given properties to be present on at least one node projection, and the properties will be streamed for all such projections.

The procedure can be configured to stream just the properties for some specific node projections. In the following example, we ran an algorithm on a sub-graph and subsequently streamed the newly created property.

Stream node properties of a specific node projection: 

CALL gds.graph.create('my-graph', ['A', 'B'], '*')
CALL gds.wcc.mutate('my-graph', {nodeLabels: ['A'], mutateProperty: 'componentId'})
CALL gds.graph.streamNodeProperties('my-graph', ['componentId'], ['A'])

When a list of projections that are not * is specified, as in the example above, a different validation and execution is applied. It is then required that all projections have all of the given properties, and they will be streamed for all of the projections.

If any of the given projections is '*', the procedure behaves like in the first example.

When streaming multiple node properties, the name of each property is included in the result. This adds with some overhead, as each property name must be repeated for each node in the result, but is necessary in order to distinguish properties. For streaming a single node property this is not necessary. gds.graph.streamNodeProperty() streams a single node property from the in-memory graph, and omits the property name. The result has the format nodeId, propertyValue, as is familiar from the streaming mode of many algorithm procedures.

Stream a single node property: 

CALL gds.graph.streamNodeProperty('my-graph', 'componentId')

We can stream relationship properties stored in a named in-memory graph back to the user. This is useful if we ran multiple algorithms in mutate mode and want to retrieve some or all of the results. This is similar to what the stream execution mode does, but allows more fine-grained control over the operations.

Stream multiple relationship properties: 

CALL gds.graph.streamRelationshipProperties('my-graph', ['similarityScore', 'weight'])

The procedure can be configured to stream just the properties for some specific relationship projections. In the following example, we ran an algorithm on a sub-graph and subsequently streamed the newly created property.

Stream relationship properties of a specific relationship projection: 

CALL gds.graph.create('my-graph', ['*'], [A', 'B'])
CALL gds.nodeSimiliarity.mutate('my-graph', {relationshipTypes: ['A'], mutateRelationshipType: 'R', mutateProperty: 'similarityScore'})
CALL gds.graph.streamNodeProperties('my-graph', ['similarityScore'], ['R'])

When a list of projections that are not * is specified, as in the example above, a different validation and execution is applied. It is then required that all projections have all of the given properties, and they will be streamed for all of the projections.

If any of the given projections is '*', the procedure behaves like in the first example.

When streaming multiple relationship properties, the name of the relationship type and of each property is included in the result. This adds with some overhead, as each type name and property name must be repeated for each relationship in the result, but is necessary in order to distinguish properties. For streaming a single relationship property, the property name can be left out. gds.graph.streamNodeProperty() streams a single relationship property from the in-memory graph, and omits the property name. The result has the format sourceNodeId, targetNodeId, relationshipType, propertyValue.

Stream a single relationship property: 

CALL gds.graph.streamRelationshipProperty('my-graph', 'similarityScore')

Similar to streaming properties stored in an in-memory graph it is also possible to write those back to Neo4j. This is similar to what the write execution mode does, but allows more fine-grained control over the operations.

The properties to write are typically the mutateProperty values that were used when running algorithms. Properties that were added to the created graph at creation time will often already be present in the Neo4j database.

CALL gds.graph.writeNodeProperties(
  graphName: String,
  nodeProperties: List<String>,
  nodeLabels: List<String>,
  configuration: Map
) YIELD
  graphName: String,
  nodeProperties: List<String>,
  writeMillis: Integer,
  propertiesWritten: Integer

CALL gds.graph.writeNodeProperties(
  'my-graph',
  ['componentId', 'pageRank', 'communityId'],
  ['*'],
  {writeConcurrency: 8}
)

CALL gds.graph.create('my-graph', ['A', 'B'], '*')
CALL gds.wcc.mutate('my-graph', {nodeLabels: ['A'], mutateProperty: 'componentId'})
CALL gds.graph.writeNodeProperties('my-graph', ['componentId'], ['A'])

We can write relationships stored in a named in-memory graph back to Neo4j. This can be used to write algorithm results (for example from Node Similarity) or relationships that have been aggregated during graph creation.

The relationships to write are specified by a relationship type. This can either be an element identifier used in a relationship projection during graph construction or the writeRelationshipType used in algorithms that create relationships. Relationships are always written using a single thread.

Write relationships to Neo4j: 

CALL gds.graph.writeRelationship('my-graph', 'SIMILAR_TO')

By default, no relationship properties will be written. To write relationship properties, these have to be explicitly specified.

Write relationships and their properties to Neo4j: 

CALL gds.graph.writeRelationship('my-graph', 'SIMILAR_TO', 'similarityScore')

We can create new Neo4j databases from named in-memory graphs stored in the graph catalog. All nodes, relationships and properties present in an in-memory graph are written to a new Neo4j database. This includes data that has been projected in gds.graph.create and data that has been added by running algorithms in mutate mode. The newly created database will be stored in the Neo4j databases directory using a given database name.

The feature is useful in the following, exemplary scenarios:

Export a named graph to a new database in the Neo4j databases directory: 

CALL gds.graph.export('my-graph', { dbName: 'mydatabase' })

The procedure yields information about the number of nodes, relationships and properties written.

The new database can be started using databases management commands.

After running the procedure, we can start a new database and query the exported graph: 

:use system
CREATE DATABASE mydatabase;
:use mydatabase
MATCH (n) RETURN n;