neo4j-gds-skill

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

Context	Use
Aura Pro with GDS plugin	This skill
Self-managed/local/offline Neo4j with GDS plugin	This skill
AuraDB serverless analytics session	neo4j-aura-graph-analytics-skill
Self-managed Neo4j attached to AGA session	neo4j-aura-graph-analytics-skill
Non-Neo4j data source	neo4j-aura-graph-analytics-skill

---

Pre-flight

Use only with embedded GDS plugin.

from graphdatascience import GraphDataScience

gds = GraphDataScience("neo4j+s://xxx.databases.neo4j.io", auth=("neo4j", "pw"))   # AuraDS; aura_ds auto-derived
gds = GraphDataScience("bolt://localhost:7687", auth=("neo4j", "password"))
print(gds.server_version())

RETURN gds.version() AS gds_version

GDS plugin unavailable: client raises GdsNotFound at construction; Cypher raises Unknown function 'gds.version'. AuraDB serverless analytics → neo4j-aura-graph-analytics-skill. Self-managed/local → install or enable GDS plugin.

pip install graphdatascience                 # Python client 2.0+
pip install "graphdatascience[rust-ext]"     # optional: faster serialization

Compatibility: graphdatascience 2.0 — GDS >= 2.13 and < 2.28 / < 2026.9, Python >= 3.10 and < 3.15, Neo4j Python driver >= 5.26 and < 7.0, pandas 2–3, pyarrow 21–25. GDS server < 2.13 → DeprecationWarning at construction; pin graphdatascience<2 (client 1.22) there.

Client 1.x fallback (GDS server < 2.13)

2.0	1.x client
gds.page_rank, gds.louvain, … — no v2 prefix	gds.v2.page_rank, gds.v2.louvain, …
gds.graph.project.native(...)	gds.v2.graph.project(...)
gds.graph.project.cypher(query)	gds.graph.cypher.project(query, database=...)
Graph / Model	GraphV2 / ModelV2
gds.graph.drop(...) → list[GraphInfo]	gds.v2.graph.drop(...) → single GraphInfo
Graph.drop(fail_if_missing=)	Graph.drop(failIfMissing=)
run_cypher(...) — always retries	run_cypher(..., retryable=...)

Migration guide: Neo4j GDS Python client 2.0 migration

GDS plugin releases track the server: 2026.08.1 requires Neo4j 2026.08 — check the GDS compatibility table before upgrading either side.

GDS plugin 2026.07.0 removed CALL gds.userLog() — read hints and warnings from driver result summary notifications or the Neo4j debug log; track task progress with CALL gds.listProgress().

2.0 client rules:

• Plain endpoints, no v2 prefix — untyped 1.x endpoints and gds.v2.* are gone
• snake_case parameters: page_rank, fast_rp, mutate_property, write_property
• Typed result attributes: result.write_millis, not result["writeMillis"] (stream still returns DataFrame)
• Server version via gds.server_version() — no gds.version() client method (Cypher RETURN gds.version() still valid)
• Procedure aliases: gds.betweenness ≡ gds.betweenness_centrality; also gds.closeness, gds.degree, gds.eigenvector, gds.harmonic, gds.kcore
• Pipelines: gds.pipeline.node_classification / link_prediction / node_regression — the only API in 2.0
• No async/job-handle API on the plugin surface — compute(), *_async, gds.jobs are AGA Sessions only

---

Graph Catalog Operations

Native Projection

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

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

G, result = gds.graph.project.native(
    "myGraph",
    {"Person": {"properties": ["age", "score"]}, "City": {}},
    {"KNOWS": {"orientation": "UNDIRECTED"}, "LIVES_IN": {"properties": ["since"]}},
    overwrite=True,   # drop same-named graph first
)

Native projection: plugin/simple Python-client workflow only. AGA Sessions → neo4j-aura-graph-analytics-skill. 1.x fallback: gds.v2.graph.project(...).

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

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

gds.graph.project.cypher(query) takes no database= — set GraphDataScience(..., database=...) at construction or call gds.set_database(...) before projecting. Query must end with exactly one RETURN gds.graph.project(...). If validation fails: use gds.run_cypher(...), then gds.graph.get("graphName"). 1.x fallback: gds.graph.cypher.project(query, database=...).

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.graph.drop(G)           # frees JVM heap; returns list[GraphInfo]

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

gds.graph.list()

Memory Estimation — run before large projections and algorithms

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

est = gds.graph.project.estimate(node_projection=["Person"], relationship_projection=["KNOWS"])
print(est.required_memory)

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

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

---

Execution Modes

Mode	Side effect	Returns	Use when
stream	None	Row per node/pair	Inspect results; top-N
stats	None	Single aggregate row	Summary/convergence check
mutate	Adds node property or relationship type/property to in-memory graph only	Stats row	Chain algorithms
write	Persists node property or relationship to Neo4j DB	Stats row	Final step — make queryable

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.page_rank.stream(G, damping_factor=0.85)
mutate_result = gds.page_rank.mutate(G, mutate_property="pagerank", damping_factor=0.85)
write_result = gds.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.louvain.stream(G)
write_result = gds.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.wcc.stream(G)
write_result = gds.wcc.write(G, write_property="componentId")
print(write_result.node_properties_written)

Betweenness Centrality

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

Node Similarity

Jaccard similarity from common neighbors — no node properties required.

gds.node_similarity.stream(G, similarity_cutoff=0.1, top_k=10)
gds.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.fast_rp.mutate(G, embedding_dimension=256, iteration_weights=[0.0, 1.0, 1.0],
                   random_seed=42, mutate_property="embedding")
write_result = gds.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.knn.stream(G, node_properties=["embedding"], top_k=10)
gds.knn.write(G, node_properties=["embedding"], top_k=10,
              write_relationship_type="SIMILAR", write_property="score")

---

FastRP → KNN Pipeline (recommendation)

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

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

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

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

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

---

Algorithm Selection

Goal	Algorithm
Influence via network links	PageRank / ArticleRank
Bottleneck / bridge nodes	Betweenness Centrality
Direct connections	Degree Centrality
Community (general, fast)	Louvain
Community (higher quality)	Leiden
Is graph connected?	WCC (run first)
Similarity from embeddings	KNN
Similarity from neighbors	Node Similarity
Shortest path (positive weights)	Dijkstra / A*
k alternative paths	Yen's
Fast scalable embeddings	FastRP
Feature-rich nodes	GraphSAGE (client: gds.graph_sage; Cypher: gds.beta.graphSage)

Full algorithm catalog → references/algorithms.md

---

Common Errors

Error	Cause	Fix
Unknown function 'gds.version'	Embedded GDS plugin unavailable	AGA → neo4j-aura-graph-analytics-skill; self-managed/local → install plugin
GdsNotFound at client construction	GDS plugin not installed on target DB	Install/enable GDS plugin; AuraDS endpoint only with GDS
AttributeError: ... no attribute 'version'	gds.version() does not exist in client 2.0	Use gds.server_version()
DeprecationWarning at client construction	GDS server < 2.13	Upgrade GDS server, or pin graphdatascience<2 and use the 1.x mapping table
Insufficient heap memory / OOM	Graph too large for available JVM heap	Run gds.graph.project.estimate; increase dbms.memory.heap.max_size
Procedure not found: gds.leiden	Older or incompatible GDS	Check CALL gds.list() for available procedures; upgrade GDS or use Louvain
Node property 'X' not found after mutate	Property not projected or wrong graph name	Verify G.node_properties() includes the property; check mutate_property spelling
Graph 'myGraph' already exists	Leftover projection from failed run	overwrite=True, CALL gds.graph.drop('myGraph'), or gds.graph.drop(G)
mutate_property already exists	Re-running algorithm on same projection	Drop and re-project, or use different mutate_property name
No algorithm results	Source/target node not in projection	Verify node labels/rel types match projection; check G.node_count()
AttributeError: 'list' object ... after gds.graph.drop(...)	2.0 returns list[GraphInfo]	Index the result; 1.x client returns a single GraphInfo

---

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.graph.project.native(...) or gds.graph.project.cypher(query).
5. Run gds.*.stream first; switch to mutate; use write only when satisfied.
6. Drop graph with gds.graph.drop(G).
7. GDS server < 2.13 → client 1.22 via the mapping table above.

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

---

MCP Tool Mapping

Operation	MCP tool
RETURN gds.version()	read-cypher
gds.pageRank.stream(...)	read-cypher
gds.pageRank.write(...)	write-cypher
gds.graph.drop(...)	write-cypher
List available procedures	read-cypher → CALL gds.list()

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.server_version() or RETURN gds.version()
• Graph/algorithm memory estimated before large work
• Python examples use 2.0 endpoints (no v2 prefix), snake_case params, typed result attributes
• Client 1.x used only with GDS server < 2.13, via the mapping table
• Projection uses native or plugin Cypher projection; no gds.graph.project.remote(...)
• Named graph dropped after use (gds.graph.drop(G); 1.x: gds.v2.graph.drop(G))
• Execution mode chosen: stream (inspect) → mutate (chain) → write (persist)
• write_property/mutate_property checked for collision with existing properties
• overwrite=True when re-projecting an existing graph name
• randomSeed set for reproducible embeddings
• WCC run first on graphs that may be disconnected