When to Use

• Running GDS algorithms against embedded GDS plugin through Python client (graphdatascience)
• Running GDS algorithms through CALL gds.* Cypher procedures
• Aura Pro, self-managed Neo4j, local Neo4j, or offline DBMS with GDS plugin installed
• Projecting named in-memory graphs, running centrality/community/similarity/path/embedding algorithms
• Chaining algorithms via mutate mode; building FastRP → KNN pipelines
• Writing node embeddings for Neo4j vector indexes / structural similarity search
• Memory estimation before large graph operations

When NOT to Use

• Aura Graph Analytics Sessions / AGA / GdsSessions / AuraGraphDataScience → neo4j-aura-graph-analytics-skill
• AuraDB Cypher API with { memory: ... } or { sessionId: ... } → neo4j-aura-graph-analytics-skill
• Cypher query authoring → neo4j-cypher-skill
• Driver/connection setup → neo4j-driver-python-skill
• GraphRAG retrieval → neo4j-graphrag-skill
• Creating/querying vector indexes over written embeddings → neo4j-vector-index-skill

Pre-flight

Use only with embedded GDS plugin.

from graphdatascience import GraphDataScience

gds = GraphDataScience("neo4j+s://xxx.databases.neo4j.io", auth=("neo4j", "pw"), aura_ds=True)
gds = GraphDataScience("bolt://localhost:7687", auth=("neo4j", "password"))
print(gds.server_version())

RETURN gds.version() AS gds_version

If Unknown function 'gds.version' → GDS plugin unavailable. AuraDB serverless analytics → neo4j-aura-graph-analytics-skill. Self-managed/local → install or enable GDS plugin.

pip install graphdatascience              # Python client
pip install graphdatascience[rust_ext]    # 3–10× faster serialization

Compatibility: graphdatascience v1.22 — GDS >= 2.6 and < 2.28 / < 2026.6, Python >= 3.10 and < 3.15, Neo4j Driver >= 4.4.12 and < 7.0.

V2 rules:

• Prefer gds.v2.* when endpoint exists.
• Use snake_case endpoints and parameters: page_rank, fast_rp, mutate_property, write_property.
• Use typed result attributes: result.write_millis, not result["writeMillis"].
• Use v1 if v2 endpoint missing/incompatible; label fallback.

Graph Catalog Operations

Native Projection

CALL gds.graph.project(
  'myGraph',
  ['Person', 'City'],
  { KNOWS: { orientation: 'UNDIRECTED' }, LIVES_IN: {} }
)
YIELD graphName, nodeCount, relationshipCount

G, result = gds.v2.graph.project("myGraph", "Person", "KNOWS")
print(result.node_count, result.relationship_count)

G, result = gds.v2.graph.project(
    "myGraph",
    {"Person": {"properties": ["age", "score"]}, "City": {}},
    {"KNOWS": {"orientation": "UNDIRECTED"}, "LIVES_IN": {"properties": ["since"]}}
)

Native projection: plugin/simple Python-client workflow only. AGA Sessions → neo4j-aura-graph-analytics-skill. V1 fallback: gds.graph.project(...).

Cypher Projection (use for new Cypher workflows, filters, transforms)

G, result = gds.graph.cypher.project(
    """
    MATCH (source:Person)-[r:KNOWS]->(target:Person)
    WHERE source.active = true
    RETURN gds.graph.project($graph_name, source, target,
        { sourceNodeProperties: source { .score }, relationshipType: 'KNOWS' })
    """,
    database="neo4j", graph_name="activeGraph"
)

gds.graph.cypher.project must end with one RETURN gds.graph.project(...) clause. If validation fails: use gds.run_cypher(...), then gds.graph.get("graphName"). Use v1 gds.graph.cypher.project(...) if v2 graph projection cannot express required filter/transform.

AGA Sessions → neo4j-aura-graph-analytics-skill; never use plugin Cypher projection.

Undirected Projection

Native projection: set orientation: 'UNDIRECTED' per relationship type. Plugin Cypher projection: set undirectedRelationshipTypes: ['*'] in fifth gds.graph.project(...) config argument.

Leiden is defined for directed and undirected graphs. Project undirected relationships when community structure is naturally symmetric.

Inspect and Drop

G.node_count()              # 12_043
G.relationship_count()      # 87_211
G.node_properties()         # projected + mutated properties by label
G.relationship_properties() # projected + mutated properties by type
G.size_in_bytes()
gds.v2.graph.drop(G)        # frees JVM heap

G = gds.v2.graph.get("myGraph")       # re-attach to existing projection

gds.v2.graph.list()

Memory Estimation — run before large projections and algorithms

CALL gds.graph.project.estimate(['Person'], 'KNOWS')
YIELD requiredMemory, bytesMin, bytesMax, nodeCount, relationshipCount

G, project_result = gds.v2.graph.project("myGraph", "Person", "KNOWS")
print(project_result.node_count)

# Algorithm estimation:
est = gds.v2.page_rank.estimate(G, damping_factor=0.85)
print(est.required_memory)

Projection estimate fallback: use v1 gds.graph.project.estimate(...) if v2 estimate endpoint unavailable.

Execution Modes

Pattern: stream to verify → mutate to chain → write to persist.

mutate_property must not exist in the in-memory graph. Relationship algorithms such as KNN also require mutate_relationship_type. After write, re-project to use written properties in subsequent GDS calls (in-memory graph does not see DB writes).

gds.util.asNode() — Enrich Stream Results

stream mode yields nodeId (internal GDS integer). gds.util.asNode(nodeId) translates it back to the DB node so you can access properties.

// Single property
CALL gds.pageRank.stream('myGraph', {})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC LIMIT 10

// Multiple properties — convert once with WITH
CALL gds.pageRank.stream('myGraph', {})
YIELD nodeId, score
WITH gds.util.asNode(nodeId) AS node, score
RETURN node.name AS name, node.born AS born, score
ORDER BY score DESC LIMIT 10

Not needed for write, mutate, or stats modes — those don't return per-node data.

Core Algorithms

PageRank (centrality)

CALL gds.pageRank.stream('myGraph', { dampingFactor: 0.85, maxIterations: 20 })
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score ORDER BY score DESC LIMIT 10
// score: relative influence — not absolute. Compare within same run only.
// didConverge: true means score stabilized; if false, increase maxIterations.

CALL gds.pageRank.write('myGraph', { writeProperty: 'pagerank', dampingFactor: 0.85 })
YIELD nodePropertiesWritten, ranIterations, didConverge

pr_df = gds.v2.page_rank.stream(G, damping_factor=0.85)
mutate_result = gds.v2.page_rank.mutate(G, mutate_property="pagerank", damping_factor=0.85)
write_result = gds.v2.page_rank.write(G, write_property="pagerank", damping_factor=0.85)
print(write_result.write_millis)

Louvain (community detection)

CALL gds.louvain.stream('myGraph', { relationshipWeightProperty: 'weight' })
YIELD nodeId, communityId

CALL gds.louvain.write('myGraph', { writeProperty: 'community' })
YIELD communityCount, modularity

louvain_df = gds.v2.louvain.stream(G)
write_result = gds.v2.louvain.write(G, write_property="community")
print(write_result.community_count)

Leiden is a refinement of Louvain avoiding poorly connected communities — use when community quality > raw speed. modularity in stats result: range -0.5 to 1.0. [field] Values > 0.3 often indicate meaningful community structure; > 0.7 is strong. Leiden is defined for directed and undirected graphs. Project undirected relationships when community structure is naturally symmetric.

WCC — Weakly Connected Components

Run WCC first to understand graph structure; partition disconnected graphs before expensive algorithms.

CALL gds.wcc.stream('myGraph', { minComponentSize: 10 })
YIELD nodeId, componentId

CALL gds.wcc.write('myGraph', { writeProperty: 'componentId' })
YIELD nodePropertiesWritten, componentCount

wcc_df = gds.v2.wcc.stream(G)
write_result = gds.v2.wcc.write(G, write_property="componentId")
print(write_result.node_properties_written)

Betweenness Centrality

gds.v2.betweenness_centrality.stream(G)          # identifies bottleneck/bridge nodes
gds.v2.betweenness_centrality.write(G, write_property="betweenness")

Node Similarity

Jaccard similarity from common neighbors — no node properties required.

gds.v2.node_similarity.stream(G, similarity_cutoff=0.1, top_k=10)
gds.v2.node_similarity.write(G, write_relationship_type="SIMILAR", write_property="score",
                             similarity_cutoff=0.1, top_k=10)

FastRP (node embeddings)

Fast, scalable, production ML pipelines. Set randomSeed for reproducibility.

CALL gds.fastRP.mutate('myGraph', {
  embeddingDimension: 256,
  iterationWeights: [0.0, 1.0, 1.0],
  featureProperties: ['score'],
  propertyRatio: 0.5,
  normalizationStrength: -0.5,
  randomSeed: 42,
  mutateProperty: 'embedding'
})
YIELD nodePropertiesWritten

gds.v2.fast_rp.mutate(G, embedding_dimension=256, iteration_weights=[0.0, 1.0, 1.0],
                      random_seed=42, mutate_property="embedding")
write_result = gds.v2.fast_rp.write(G, embedding_dimension=256, write_property="embedding",
                                    random_seed=42)
print(write_result.write_millis)

For ANN search over structural embeddings, after write, create a Neo4j vector index over the written property. Use neo4j-vector-index-skill.

KNN — K-Nearest Neighbors

Finds k most similar nodes per node based on node properties (typically embeddings).

CALL gds.knn.stream('myGraph', {
  nodeProperties: ['embedding'], topK: 10,
  sampleRate: 0.5, similarityCutoff: 0.7
})
YIELD node1, node2, similarity

CALL gds.knn.write('myGraph', {
  nodeProperties: ['embedding'], topK: 10,
  writeRelationshipType: 'SIMILAR', writeProperty: 'score'
})
YIELD relationshipsWritten

knn_df = gds.v2.knn.stream(G, node_properties=["embedding"], top_k=10)
gds.v2.knn.write(G, node_properties=["embedding"], top_k=10,
                 write_relationship_type="SIMILAR", write_property="score")

FastRP → KNN Pipeline (recommendation)

# 1. Project
G, _ = gds.v2.graph.project("myGraph", "Product",
    {"BOUGHT_TOGETHER": {"orientation": "UNDIRECTED"}})

# 2. Estimate memory
print(gds.v2.fast_rp.estimate(G, embedding_dimension=128).required_memory)

# 3. Embed
gds.v2.fast_rp.mutate(G, embedding_dimension=128, random_seed=42, mutate_property="emb")

# 4. Similarity
gds.v2.knn.write(G, node_properties=["emb"], top_k=10,
                 write_relationship_type="SIMILAR", write_property="score")

# 5. Cleanup
gds.v2.graph.drop(G)

Algorithm Selection

Full algorithm catalog → references/algorithms.md

Common Errors

Full Workflow

1. Create gds with GraphDataScience(...).
2. Verify plugin: gds.server_version() or RETURN gds.version().
3. Estimate memory: gds.graph.project.estimate(...) and algorithm .estimate(...).
4. Project named graph with gds.v2.graph.project(...).
5. Run gds.v2.*.stream first; switch to mutate; use write only when satisfied.
6. Drop graph with gds.v2.graph.drop(G).
7. Use v1 only for endpoints missing in v2, such as plugin Cypher projection.

Built-in test datasets: gds.v2.graph.datasets.load_cora(), gds.v2.graph.datasets.load_karate_club(), gds.v2.graph.datasets.load_imdb()

MCP Tool Mapping

Before any write-cypher: show exact Cypher, expected nodes/relationships affected, and ask for confirmation. For algorithm write mode, estimate or run stats first when available.

References

• references/algorithms.md — full algorithm catalog: all procedures, parameters, tiers, Cypher + Python examples
• references/graph-projection.md — projection deep-dive: filtering, heterogeneous graphs, relationship orientation, property types
• GDS Manual
• Python Client Docs

Checklist

• Embedded GDS plugin confirmed with gds.version() or gds.server_version()
• Graph/algorithm memory estimated before large work
• Python examples prefer gds.v2.*, snake_case params, typed result attributes
• v1 APIs used only as explicit fallback
• Projection uses native or plugin Cypher projection; no gds.graph.project.remote(...)
• Named graph dropped after use (gds.v2.graph.drop(G) or v1 fallback)
• Execution mode chosen: stream (inspect) → mutate (chain) → write (persist)
• write_property/mutate_property checked for collision with existing properties
• randomSeed set for reproducible embeddings
• WCC run first on graphs that may be disconnected