GraphSAGE node embedding prediction

In order to apply GraphSAGE node embedding prediction, one must first have trained a GraphSAGE node embedding model using the GraphSAGE node embedding training endpoint.

This page provides instructions for how to use the GraphSAGE node embedding prediction endpoint to infer embeddings for new nodes.

Syntax

This section covers the syntax used to execute the GraphSAGE node embedding prediction algorithm.

Run GraphSAGE node embedding prediction.
CALL graph.gs_unsup_predict(
  'CPU_X64_XS',                    (1)
  {
    ['defaultTablePrefix': '...',] (2)
    'project': {...},              (3)
    'compute': {...},              (4)
    'write':   {...}               (5)
  }
);
1 Compute pool selector.
2 Optional prefix for table references.
3 Project config.
4 Compute config.
5 Write config.
Table 1. Parameters
Name Type Default Optional Description

computePoolSelector

String

n/a

no

The selector for the compute pool on which to run the GraphSAGE node embedding prediction job.

configuration

Map

{}

no

Configuration for graph project, algorithm compute and result write back.

For this algorithm we recommend using a GPU compute pool if the input graph is large or the model is deep, but otherwise it might be possible to get away with a CPU compute pool.

The configuration map consists of the following three entries.

For more details on below Project configuration, refer to the Project documentation.
Table 2. Project configuration
Name Type

nodeTables

List of node tables.

relationshipTables

Map of relationship types to relationship tables.

Please note that in order for GraphSAGE to properly propagate updates of node embeddings, each type of node must be the target of at least one relationship type. The orientation parameter can be useful to add reverse direction relationships for types of nodes that are only the source of relationships (using the "REVERSE" or "UNDIRECTED" orientations).

Table 3. Compute configuration
Name Type Default Optional Description

modelname

String

n/a

no

The name of the trained model to use

batchSize

Integer

Inherited

yes

The number of target nodes to predict on in each batch. If not provided, the batch size that was used when training the model will be used

randomSeed

Integer

A random integer

yes

A number used to seed all randomness of the computation
For more details on below Write configuration, refer to the Write documentation.
Table 4. Write configuration
Name Type Default Optional Description

nodeLabel

String

n/a

no

Node label in the in-memory graph from which to write a node property.

outputTable

String

n/a

no

Table in Snowflake database to which node properties are written.

Examples

For our example we will use an IMDB dataset with actors, directors, movies, and genres. These all have keywords associated with them, which we will use as features for the nodes. They are connected by relationships where actors act in movies and directors direct movies.

We have a database called imdb that contains the tables:

  • actor with columns nodeid and plot_keywords

  • movie with columns nodeid and plot_keywords

  • director with columns nodeid and plot_keywords

  • acted_in with columns sourcenodeid and targetnodeid that represent actor and movie node IDs

  • directed_in with columns sourcenodeid and targetnodeid that represent director and movie node IDs

The plot_keywords columns contain keywords associated with the nodes, encoded as vectors of floats.

You can upload this dataset to your snowflake account by following the instructions at github: neo4j-product-examples/snowflake-graph-analytics.

The prediction query

We assume a model named unsup-imdb has been trained using the GraphSAGE node embedding training endpoint (see the example).

In the following predict query we specify the project configuration as we did during training. We only need to specify the modelname in the compute configuration as the rest is inherited from the training configuration.

Please also note that we provide write configuration to specify the tables where the computed embeddings will be stored.

To run the query, there is a required setup of grants for the application, your consumer role and your environment. Please see the Getting started page for more on this.

We also assume that the application name is the default Neo4j_Graph_Analytics. If you chose a different app name during installation, please replace it with that.

CALL Neo4j_Graph_Analytics.graph.gs_unsup_predict('GPU_NV_S', {
    'defaultTablePrefix': 'imdb.gml',
    'project': {
        'nodeTables': ['actor', 'director', 'movie'],
        'relationshipTables': {
            'acted_in': {
                'sourceTable': 'actor',
                'targetTable': 'movie',
                'orientation': 'UNDIRECTED'
            },
            'directed_in': {
                'sourceTable': 'director',
                'targetTable': 'movie',
                'orientation': 'UNDIRECTED'
            }
        }
    },
    'compute': {
        'modelname': 'unsup-imdb'
    },
    'write': [
        {
            'nodeLabel': 'movie',
            'outputTable': 'movie_embeddings'
        },
        {
            'nodeLabel': 'actor',
            'outputTable': 'actor_embeddings'
        }
    ]
});

The above query should produce a result similar to the one below.

JOB_ID

JOB_START

JOB_END

JOB_RESULT

job_7c5303c7899547e5b71f42000393ea59

2025-04-29 13:02:41.287

2025-04-29 13:03:20.554

{ "node_output_stats": { "actor": { "row_count": 5841, "table_name": "imdb.gml.actor_embeddings" }, "movie": { "row_count": 4661, "table_name": "imdb.gml.movie_embeddings" } } }

We can inspect the produced embeddings for the "first" two actor nodes by running

SELECT * FROM IMDB.GML.actor_embeddings LIMIT 2;

which yields

NODEID	EMBEDDING
  6931	[0.000000,0.275806,0.333710,0.000000,0.000000,0.000000,1.678880,0.000000,0.000000,0.000000,1.548835,0.705060,0.698967,0.000000,0.000000,0.748005,0.000000,0.000000,0.032963,0.000000,0.000000,0.000000,0.000000,1.082358,0.000000,0.000000,0.000000,0.168949,0.000000,2.504213,1.462919,0.125596,0.000000,0.000000,0.000000,0.020546,1.581930,0.000000,0.000000,2.310814,0.071558,2.554588,0.148772,0.000000,1.342975,0.522795,0.000000,0.664341,0.341649,0.280214,0.000000,0.923817,0.386520,0.000000,1.193098,0.915913,0.000000,0.000000,0.000000,0.538325,0.819232,0.000000,0.594658,0.000000,0.933429,0.000000,0.665206,0.000000,0.000000,0.577315,1.039834,0.000000,0.000000,0.000000,0.000000,0.000000,0.263641,1.260790,0.000000,0.285769,0.797815,0.143350,0.000000,0.000000,0.000000,0.760788,0.537654,0.835502,0.000000,0.000000,0.000000,0.000000,0.000000,2.075793,0.000000,0.000000,0.000000,0.000000,0.284028,0.000000,0.000000,0.979259,0.000000,0.000000,0.000000,0.000000,0.855099,0.100875,0.000000,1.053404,0.785612,1.723292,0.750773,0.000000,1.061991,0.000000,0.000000,0.345646,0.000000,0.000000,0.068293,0.208700,0.345753,0.000000,1.941105,1.733489,0.000000,1.319209,0.867830,0.000000,0.363041,0.000000,2.042518,0.490823,1.638796,0.181801,1.475707,0.669406,0.000000,0.000000,0.000000,0.000000,1.641088,0.000000,0.400986,0.173700,0.598307,0.000000,1.213337,2.599229,0.140189,0.758646,0.000000,0.462205,0.000000,0.843118,0.000000,0.480571,0.000000,0.000000,1.049532,1.766646,0.000000,1.403183,0.461133,0.000000,1.863557,0.000000,0.325326,0.000000,0.430773,0.000000,0.000000,0.405159,0.000000,0.056346,0.000000,0.000000,0.368162,0.000000,0.104882,0.000000,1.468014,1.360908,1.744029,0.000000,0.000000,1.179452,0.547919,0.000000,0.000000,1.504631,0.045264,0.644895,0.000000,0.000000,0.000000,0.101866,0.696652,0.000000,0.000000,0.000000,0.000000,0.000000,0.464310,0.218352,1.510026,0.000000,0.000000,0.000000,0.246313,0.000000,0.178400,0.000000,0.000000,0.000000,0.000000,0.701618,0.070749,0.000000,0.000000,0.603074,0.000000,0.000000,0.000000,1.001244,0.465114,0.570518,0.000000,0.000000,0.932442,0.325107,0.622439,1.470797,0.000000,0.316309,0.000000,0.247825,0.270058,0.946190,0.000000,0.000000,0.000000,1.873032,0.000000,0.000000,1.510353,0.562894,0.000000,1.618610,0.322268,0.000000,0.000000,0.512696,1.013735,0.036263]
  6932	[0.000000,0.000000,0.530572,0.998598,0.000000,0.000000,1.851767,0.000000,0.000000,0.000000,1.551337,0.213426,1.226383,0.000000,0.000000,0.498449,0.364041,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.162470,1.721648,0.000000,0.000000,0.146792,0.460837,0.000000,0.542702,0.000000,0.000000,0.585533,0.000000,0.859640,0.915765,0.000000,0.837624,0.101824,0.000000,0.000000,0.000000,0.000000,0.000000,0.766046,1.116825,0.000000,1.394197,0.347508,0.319338,0.000000,0.000000,0.000000,0.669600,0.000000,0.554313,0.014044,0.573868,0.000000,0.214537,0.000000,0.000000,1.127173,0.000000,0.000000,0.000000,0.000000,0.000000,0.535387,0.000000,1.786094,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.804344,2.133009,0.948304,0.000000,0.000000,0.000000,0.000000,0.637896,1.149579,0.000000,0.000000,0.412694,0.507935,0.385693,0.979215,0.000000,1.737015,0.194008,0.000000,0.000000,0.000000,0.793839,1.421982,0.000000,0.228598,1.145930,0.539003,0.643983,0.897991,0.956065,0.000000,0.000000,0.000000,0.000000,0.000000,1.778283,1.129515,0.000000,0.280099,2.112762,2.177783,0.000000,0.820926,0.000000,0.000000,0.000000,0.000000,1.528605,0.000000,1.808216,0.539503,1.489559,0.437647,0.000000,0.000000,0.000000,1.395121,1.636038,0.000000,0.741754,0.000000,0.000000,0.118127,1.866470,1.642432,0.452591,1.244799,0.478923,1.421850,0.294502,1.133924,0.000000,0.000000,0.000000,0.000000,1.080479,0.568388,0.440139,1.385756,0.000000,0.000000,0.618767,0.000000,0.085425,0.000000,0.000000,1.528592,0.011202,1.152845,0.578253,0.000000,0.000000,1.087868,0.960426,0.000000,0.000000,0.000000,2.200223,2.432830,1.850158,0.238872,0.000000,0.000000,0.391092,0.000000,0.000000,0.612836,0.583207,0.000000,0.000000,0.281198,1.414375,0.000000,0.709002,0.000000,0.000000,0.000000,0.000000,0.000000,0.141193,0.231656,0.109030,0.277832,0.000000,0.000000,0.000000,0.449620,0.000000,0.000000,0.000000,0.000000,0.000000,0.660041,0.000000,0.378508,0.000000,0.000000,0.501338,0.000000,0.194963,0.765173,0.227336,0.000000,0.000000,0.223111,0.972678,0.485203,0.709181,0.705317,0.000000,0.661023,0.000000,0.000000,0.000000,0.458935,0.000000,0.000000,0.587035,1.170718,0.913786,0.000000,1.510381,0.428103,0.000000,1.663107,0.000000,0.477148,0.175759,0.983973,0.315653,0.736073]