Constructing a graph from DataFrames

In addition to projecting a graph from the Neo4j database, it is also possible to create graphs directly from pandas DataFrame objects.

Syntax

Table 1. Graph construct signature
Name	Type	Default	Description
`graph_name`	`str`	`-`	Name of the graph to be constructed.
`nodes`	`Union[DataFrame, List[DataFrame]]`	`-`	One or more dataframes containing node data.
`relationships`	`Union[DataFrame, List[DataFrame]]`	`-`	One or more dataframes containing relationship data.
`concurrency`	`int`	`4`	Number of threads used to construct the graph.
`undirected_relationship_types`	`Optional[List[str]]`	`None`	List of relationship types to be projected as undirected.

Example

nodes = pandas.DataFrame(
    {
        "nodeId": [0, 1, 2, 3],
        "labels":  ["A", "B", "C", "A"],
        "prop1": [42, 1337, 8, 0],
        "otherProperty": [0.1, 0.2, 0.3, 0.4]
    }
)

relationships = pandas.DataFrame(
    {
        "sourceNodeId": [0, 1, 2, 3],
        "targetNodeId": [1, 2, 3, 0],
        "relationshipType": ["REL", "REL", "REL", "REL"],
        "weight": [0.0, 0.0, 0.1, 42.0]
    }
)

G = gds.graph.construct(
    "my-graph",      # Graph name
    nodes,           # One or more dataframes containing node data
    relationships    # One or more dataframes containing relationship data
)

assert "REL" in G.relationship_types()

The above example creates a graph from two DataFrame objects, one for nodes and one for relationships. The projected graph is equivalent to a graph that the following Cypher query would create in a Neo4j database:

CREATE
    (a:A {prop1: 42,    otherProperty: 0.1),
    (b:B {prop1: 1337,  otherProperty: 0.2),
    (c:C {prop1: 8,     otherProperty: 0.3),
    (d:A {prop1: 0,     otherProperty: 0.4),

    (a)-[:REL {weight: 0.0}]->(b),
    (b)-[:REL {weight: 0.0}]->(c),
    (c)-[:REL {weight: 0.1}]->(d),
    (d)-[:REL {weight: 42.0}]->(a),

The supported format for the node data frames is described in Arrow node schema and the format for the relationship data frames is described in Arrow relationship schema.

Apache Arrow flight server support

The construct method can utilize the Apache Arrow Flight Server of GDS if it’s enabled.

For Aura Graph Analytics and AuraDS, Apache Arrow is always enabled.

This in particular means that:

The construction of the graph is greatly sped up,
It is possible to supply more than one data frame, both for nodes and relationships. If multiple node dataframes are used, they need to contain distinct node ids across all node data frames.
Prior to the construct call, a call to GraphDataScience.set_database must have been made to explicitly specify which Neo4j database should be targeted.

Limitations on community edition

For users of GDS community edition, performance can be impacted for large graphs. It is possible that socket connection with the database times out. If this happens, a possible workaround is to modify the server configuration server.bolt.connection_keep_alive or server.bolt.connection_keep_alive_probes. However, be aware of the side effects such as a genuine connection issue now taking longer to be detected.