Graph procedures¶

Listing of all graph procedures in the Neo4j Graph Data Science Python Client API. This includes all the methods for projecting, deleting, and listing graphs, as well as methods for handling node or relationship properties. These all assume that an object of GraphDataScience is available as gds.

gds.graph.construct(graph_name: str, nodes: DataFrame | List[DataFrame], relationships: DataFrame | List[DataFrame] | None = None, concurrency: int = 4, undirected_relationship_types: List[str] | None = None) → Graph¶: Constructs a new graph in the graph catalog, using the provided node and relationship data frames.

gds.graph.get(graph_name: str) → Graph¶: Gets a graph object representing a graph in the graph catalog.

gds.alpha.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) → Series[Any]¶: Removes a graph property from a projected graph.

gds.alpha.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) → DataFrame¶: Streams the given graph property.

gds.alpha.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) → Series[Any]¶
Mutates the in-memory graph with the given node Label.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.nodeLabel.mutate() instead.

gds.alpha.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) → Series[Any]¶
Writes the given node Label to an online Neo4j database.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.nodeLabel.write() instead.

gds.beta.graph.export.csv(G: Graph, **config: Any) → Series[Any]¶: Exports a named graph to CSV files.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.export.csv() instead.

gds.beta.graph.export.csv.estimate(G: Graph, **config: Any) → Series[Any]¶: Estimate the required disk space for exporting a named graph to CSV files.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.export.csv.estimate() instead.

gds.beta.graph.generate(graph_name: str, node_count: int, average_degree: int, **config: Any) → GraphCreateResult¶: Computes a random graph, which will be stored in the graph catalog.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.generate() instead.

gds.beta.graph.project.subgraph(graph_name: str, from_G: Graph, node_filter: str, relationship_filter: str, **config: Any) → GraphCreateResult¶: Filters down a graph projection to a named subgraph projection in the catalog for use by algorithms.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.filter() instead.

gds.beta.graph.relationships.stream(G: Graph, relationship_types: List[str] = ['*'], **config: Any) → TopologyDataFrame¶: Streams the given relationship source/target pairs

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.relationships.stream() instead.

gds.beta.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) → Series[Any]¶: The ToUndirected procedure converts directed relationships to undirected relationships

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.relationships.toUndirected() instead.

gds.beta.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) → Series[Any]¶: Returns an estimation of the memory consumption for that procedure.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.relationships.toUndirected.estimate() instead.

gds.graph.cypher.project(query: str, database: str | None, **params: Any) → GraphCreateResult¶: Use Cypher projection to creates a named graph in the catalog for use by algorithms. The provided query must end with a RETURN gds.graph.project(…) call.

gds.graph.deleteRelationships(G: Graph, relationship_type: str) → Series[Any]¶: Delete the relationship type for a given graph stored in the graph-catalog.

gds.graph.drop(G: Graph | str, failIfMissing: bool = False, dbName: str = '', username: str | None = None) → Series[Any] | None¶: Drops a named graph from the catalog and frees up the resources it occupies.

gds.graph.exists(graph_name: str) → Series[Any]¶: Checks if a graph exists in the catalog.

gds.graph.export(G: Graph, **config: Any) → Series[Any]¶: Exports a named graph into a new offline Neo4j database.

gds.graph.export.csv(G: Graph, **config: Any) → Series[Any]¶: Exports a named graph to CSV files.

gds.graph.export.csv.estimate(G: Graph, **config: Any) → Series[Any]¶: Estimate the required disk space for exporting a named graph to CSV files.

gds.graph.generate(graph_name: str, node_count: int, average_degree: int, **config: Any) → GraphCreateResult¶: Computes a random graph, which will be stored in the graph catalog.

gds.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) → Series[Any]¶: Removes a graph property from a projected graph.

gds.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) → DataFrame¶: Streams the given graph property.

gds.graph.list(G: Graph | str | None = None) → DataFrame¶: Lists information about named graphs stored in the catalog.

gds.graph.nodeProperties.drop(G: Graph, node_properties: List[str], **config: Any) → Series[Any]¶: Removes node properties from a projected graph.

gds.graph.nodeProperties.stream(G: Graph, node_properties: List[str] | str, node_labels: Strings = ['*'], separate_property_columns: bool = False, db_node_properties: List[str] = [], **config: Any) → DataFrame¶: Streams the given node properties.

gds.graph.nodeProperties.write(G: Graph, node_properties: List[str], node_labels: Strings = ['*'], **config: Any) → Series[Any]¶: Writes the given node properties to an online Neo4j database.

gds.graph.nodeProperty.stream(G: Graph, node_properties: str, node_labels: Strings = ['*'], db_node_properties: List[str] = [], **config: Any) → DataFrame¶: Streams the given node property.

gds.graph.project(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) → GraphCreateResult¶: Creates a named graph in the catalog for use by algorithms.

gds.graph.project.cypher(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) → GraphCreateResult¶: Creates a named graph in the catalog for use by algorithms.

gds.graph.project.cypher.estimate(node_projection: Any, relationship_projection: Any, **config: Any) → Series[Any]¶: Returns an estimation of the memory consumption for that procedure.

gds.graph.project.estimate(node_projection: Any, relationship_projection: Any, **config: Any) → Series[Any]¶: Returns an estimation of the memory consumption for that procedure.

Deprecated since version 2.5.0: Since GDS server version 2.5.0 you should use the endpoint gds.graph.filter() instead.

gds.graph.relationships.stream(G: Graph, relationship_types: List[str] = ['*'], **config: Any) → TopologyDataFrame¶: Streams the given relationship source/target pairs

gds.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) → Series[Any]¶: The ToUndirected procedure converts directed relationships to undirected relationships

gds.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) → Series[Any]¶: Returns an estimation of the memory consumption for that procedure.

gds.graph.relationship.write(G: Graph, relationship_type: str, relationship_property: str = '', **config: Any) → Series[Any]¶: Writes the given relationship and an optional relationship property to an online Neo4j database.

gds.graph.relationshipProperties.stream(G: Graph, relationship_properties: List[str], relationship_types: str | List[str] = ['*'], separate_property_columns: bool = False, **config: Any) → DataFrame¶: Streams the given relationship properties.

gds.graph.relationshipProperties.write(G: Graph, relationship_type: str, relationship_properties: List[str], **config: Any) → DataFrame¶: Write the given relationship properties back to the database.

gds.graph.relationshipProperty.stream(G: Graph, node_properties: str, node_labels: str | List[str] = ['*'], **config: Any) → DataFrame¶: Streams the given relationship property.

gds.graph.relationships.drop(G: Graph, relationship_type: str) → Series[Any]¶: Delete the relationship type for a given graph stored in the graph-catalog.

gds.graph.removeNodeProperties(G: Graph, node_properties: List[str], **config: Any) → Series¶: Removes node properties from a projected graph.

gds.graph.streamNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ['*'], separate_property_columns: bool = False, **config: Any) → DataFrame¶: Streams the given node properties.

gds.graph.streamNodeProperty(G: Graph, node_properties: str, node_labels: Strings = ['*'], **config: Any) → DataFrame¶: Streams the given node property.

gds.graph.streamRelationshipProperties(G: Graph, relationship_properties: List[str], relationship_types: Strings = ['*'], separate_property_columns: bool = False, **config: Any) → DataFrame¶: Streams the given relationship properties.

gds.graph.streamRelationshipProperty(G: Graph, relationship_properties: str, relationship_types: Strings = ['*'], **config: Any) → DataFrame¶: Streams the given relationship property.

gds.graph.writeNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ['*'], **config: Any) → Series[Any]¶: Writes the given node properties to an online Neo4j database.

gds.graph.writeRelationship(G: Graph, relationship_type: str, relationship_property: str = '', **config: Any) → Series[Any]¶: Writes the given relationship and an optional relationship property to an online Neo4j database.

gds.graph.load_cora(graph_name: str = 'cora', undirected: bool = False) → Graph¶: Loads the Cora dataset into a named graph in the catalog for use by algorithms.

gds.graph.load_karate_club(graph_name: str = 'karate_club', undirected: bool = False) → Graph¶: Loads the Karate Club dataset into a named graph in the catalog for use by algorithms.

gds.graph.load_imdb(graph_name: str = 'imdb', undirected: bool = True) → Graph¶: Loads the IMDB dataset into a named graph in the catalog for use by algorithms.

gds.graph.load_lastfm(graph_name: str = 'lastfm', undirected: bool = True) → Graph¶: Loads the LastFM dataset into a named graph in the catalog for use by algorithms.

gds.graph.ogbn.load(dataset_name: str, dataset_root_path: str = 'dataset', graph_name: str | None = None, concurrency: int = 4) → Graph¶: Loads a OGBN dataset into a named graph in the catalog for use by algorithms.

gds.graph.ogbl.load(dataset_name: str, dataset_root_path: str = 'dataset', graph_name: str | None = None, concurrency: int = 4) → Graph¶: Loads a OGBL dataset into a named graph in the catalog for use by algorithms.

gds.graph.networkx.load(nx_G: nx.Graph, graph_name: str, concurrency: int = 4) → Graph¶: Loads a NetworkX graph into a named graph in the catalog for use by algorithms.

gds.find_node_id(labels: List[str] = [], properties: Dict[str, Any] = {}) → int¶: Finds a node id by its labels and properties.

gds.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) → Series[Any]¶
Mutates the in-memory graph with the given node Label.

gds.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) → Series[Any]¶
Writes the given node Label to an online Neo4j database.

gds.graph.filter(graph_name: str, from_G: Graph, node_filter: str, relationship_filter: str, **config: Any) → GraphCreateResult¶: Filters down a graph projection to a named subgraph projection in the catalog for use by algorithms.