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| nodePropertySchema | yes | Mapping of node property names to their types.
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| relationshipPropertySchema | yes | Mapping of relationship property names to their types.
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| concurrency | yes | Concurrency to use for building the graph within the session.
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| undirectedRelationshipTypes | yes | List of relationship type names that should be treated as undirected.
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| inverseIndexedRelationshipTypes | yes | List of relationship type names that should be indexed in reverse.
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|===
@@ -165,15 +163,6 @@ Instead, the graph name is provided to the `gds.graph.project()` endpoint.
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For full details and examples on how to write Cypher projection queries, see the https://neo4j.com/docs/graph-data-science/current/management-ops/graph-creation/graph-project-cypher-projection/[Cypher projection documentation in the GDS Manual].
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==== Property schemas
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The optional parameters `nodePropertySchema` and `relationshipPropertySchema` are useful when projecting graphs with multiple node labels or relationship types which have distinct property sets (_heterogeneous graphs_).
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If these parameters are not provided, the type of each property is inferred from the first row of data seen by the projection function.
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For homogeneous graphs this is usually sufficient, while for heterogeneous graphs it is not.
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For this reason, we recommend to always use the property schema parameters.
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==== Relationship type undirectedness and inverse indexing
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The optional parameters `undirectedRelationshipTypes` and `inverseIndexedRelationshipTypes` are used to configure undirectedness and inverse indexing of relationships.
@@ -226,8 +215,8 @@ With the `gds` GDS Session active, project the graph and specify node and relati
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from graphdatascience.session import GdsPropertyTypes
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G, result = gds.graph.project(
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"my-graph",
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"""
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graph_name="my-graph",
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query="""
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CALL {
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MATCH (u1:User)
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OPTIONAL MATCH (u1)-[r:KNOWS]->(u2:User)
@@ -246,8 +235,6 @@ G, result = gds.graph.project(
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