The graph object

In order to utilize most the functionality in GDS, you must first project a graph into the GDS Graph Catalog. When projecting a graph with the Python client, a client-side reference to the projected graph is returned. We call these references Graph objects.

Once created, the Graph objects can be passed as arguments to other methods in the Python client, for example for running algorithms or training machine learning models. Additionally, the Graph objects have convenience methods allowing for inspection of the projected graph represented without explicitly involving the graph catalog.

In the examples below we assume that we have an instantiated GraphDataScience object called gds. Read more about this in Getting started.

1. Constructing a graph object

There are several ways of constructing a graph object. The simplest way is to do a native projection:

G, res = gds.graph.project(
    "my-graph",                 #  Graph name
    ["MyLabel", "YourLabel"],   #  Node projection
    "MY_REL_TYPE",              #  Relationship projection
    concurrency=4               #  Configuration parameters

where G is a Graph object, and res is a pandas Series containing metadata from the underlying procedure call.

Note that all projection syntax variants are supported by way of specifying a Python dict or list for the node and relationship projection arguments. To specify configuration parameters corresponding to the keys of the procedure’s configuration map, we give named keyword arguments, like for concurrency=4 above. Read more about this in Syntax.

Similarly to Cypher there’s also a corresponding gds.graph.project.estimate method that can be called in an analogous way.

To get a graph object that represents a graph that has already been projected into the graph catalog, one can call the client-side only get method and passing it a name:

G = gds.graph.get("my-graph")

In addition to those aforementioned there are three more methods that construct graph objects:

  • gds.graph.project.cypher

  • gds.beta.graph.subgraph

  • gds.beta.graph.generate

Their Cypher signatures map to Python in much the same way as gds.graph.project above.

2. Inspecting a graph object

There are convenience methods on the graph object that let us extract information about our projected graph.

Table 1. Graph object methods
Name Arguments Return type Description




The name of the projected graph.




The node count of the projected graph.




The relationship count of the projected graph.


label: str


A list of the node properties present on the nodes with the node label provided as input.


type: str


A list of the relationship properties present on the relationships with the relationship type provided as input.




The average out-degree of generated nodes.




Density of the graph.




Number of bytes used in the Java heap to store the graph.




Human-readable description of size_in_bytes.




Returns True if the graph exists in the GDS Graph Catalog, otherwise False.




Removes the graph from the GDS Graph Catalog.

For example, to get the node count and node properties of a graph G, we would do the following:

n = G.node_count()
props = G.node_properties("MyLabel")

3. Using a graph object

The primary use case for a graph object is to pass it to algorithms. The syntax for doing that follows the standard Cypher API, where the graph is the first parameter passed to the algorithm.

Syntax composition:
result = gds[.<tier>].<algorithm>.<execution-mode>[.<estimate>](
  G: Graph,
  **configuration: dict[str, any]

For example, to run the WCC on a graph G, and then drop the graph, do the following:

G, _ = gds.graph.project(...)

res =

gds.graph.drop(G)  # same as G.drop()

In most Cypher operations where a graph name is required, the graph object is used in the Python client instead. In some cases where this does not make sense, such as for gds.graph.exist(), where a graph name string is used instead.