neo4j-aura-graph-analytics-skill

When to Use

• Running GDS algorithms in Aura Graph Analytics GDS Sessions
• Creating GdsSessions or using AuraGraphDataScience
• Remote projecting connected Neo4j data with gds.graph.project.remote(...)
• Using AuraDB Cypher API projection with { memory: ... } or { sessionId: ... }
• Processing graph data from non-Neo4j sources (Pandas, Spark, CSV)
• On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
• Full isolation from the live database during analytics

When NOT to Use

• Aura Pro with embedded GDS plugin → neo4j-gds-skill
• Self-managed Neo4j with embedded GDS plugin → neo4j-gds-skill
• Writing Cypher queries → neo4j-cypher-skill
• Snowflake Graph Analytics → neo4j-snowflake-graph-analytics-skill

Deployment Decision Table

Defaults

• graphdatascience >= 2.0 required; >= 2.2 recommended
• 2.0 endpoints: no v2 prefix — gds.page_rank.*, gds.graph.node_properties.*, gds.graph.construct(...)
• Use snake_case parameters end-to-end
• Call gds.verify_connectivity() after session creation — verifies session and, if attached, the source DB
• Estimate memory before large sessions
• Set TTL; default 1h idle, max 7d (hard 7-day lifetime cap)
• Close session when done: gds.delete() or sessions.delete(session_name=...) stops billing
• Use AuraAPICredentials.from_env() and DbmsConnectionInfo.from_env() — never hardcode credentials

Installation

pip install "graphdatascience>=2.2"     # 2.2 is the current stable release

2.0–2.2 require: Python >= 3.10, neo4j driver 5.26–7.0, pandas 2–3, pyarrow 21–25, numpy <3.

Client 1.x (legacy)

2.0 renamed/reorganized the client. Pinned to 1.22 (graphdatascience<2)? Map:

Migration guide: Neo4j GDS Python client 2.0 migration

2.0 additions: GdsSessions.estimate(algorithms=[...]) per-algorithm memory; GdsSessions.get_or_create(show_progress=...); keyword-only GdsSessions.delete(session_name=|session_id=) returns False when nothing deleted; overwrite=True on gds.graph.project / generate / construct / filter / sample drops a same-named graph first; gds.graph.drop(...) accepts multiple graphs → list[GraphInfo].

2.1 additions: gds.run_cypher(query, auto_commit=True) for CALL { … } IN TRANSACTIONS (2.0 default retryable transaction rejects it); gds.db_driver() → session client's managed neo4j.Driver (closed by gds.close()); mode="READ"/"WRITE" strings accepted for QueryMode.

Key Patterns

Step 1 — Authenticate

from graphdatascience.session import AuraAPICredentials, GdsSessions

sessions = GdsSessions(api_credentials=AuraAPICredentials.from_env())
# Reads: AURA_CLIENT_ID, AURA_CLIENT_SECRET, AURA_PROJECT_ID (optional)
# Create API credentials in Aura Console → Account → API credentials

Member of multiple projects or organizations: set AURA_PROJECT_ID or pass project_id= — 2.1 checks organizations first when deriving the default project.

Step 2 — Estimate Memory

from graphdatascience.session import AlgorithmCategory, SessionMemory

# Per-algorithm + config — preferred
memory = sessions.estimate(
    node_count=1_000_000,
    relationship_count=5_000_000,
    algorithms=["wcc", "louvain", "fast_rp"],
)
# or with config:
memory = sessions.estimate(
    node_count=1_000_000,
    relationship_count=5_000_000,
    algorithms={"fast_rp": {"embedding_dimension": 128}},
)
# Coarse category estimate — 1.x style, still available
memory = sessions.estimate(
    node_count=1_000_000,
    relationship_count=5_000_000,
    algorithm_categories=[
        AlgorithmCategory.CENTRALITY,
        AlgorithmCategory.NODE_EMBEDDING,
        AlgorithmCategory.COMMUNITY_DETECTION,
    ],
)
# Returns SessionMemory tier, e.g. SessionMemory.m_8GB
# Fixed tiers: m_2GB … m_512GB — see references/limitations.md

Step 3 — Create Session

Mode A — AuraDB connected:

from graphdatascience.session import DbmsConnectionInfo, SessionMemory, CloudLocation
from datetime import timedelta

# Reads: AURA_INSTANCEID (takes precedence) or NEO4J_URI, plus NEO4J_USERNAME,
# NEO4J_PASSWORD, NEO4J_DATABASE
db_connection = DbmsConnectionInfo.from_env()
# Explicit: DbmsConnectionInfo(aura_instance_id=..., username=..., password=...)

gds = sessions.get_or_create(
    session_name="my-analysis",
    memory=memory,
    db_connection=db_connection,
    ttl=timedelta(hours=2),
)
gds.verify_connectivity()

Mode B — Self-managed Neo4j:

# Same from_env() — set NEO4J_URI (e.g. "bolt://my-server:7687"), no AURA_INSTANCEID
gds = sessions.get_or_create(
    session_name="my-analysis-sm",
    memory=SessionMemory.m_8GB,
    db_connection=DbmsConnectionInfo.from_env(),
    ttl=timedelta(hours=2),
    cloud_location=CloudLocation("gcp", "europe-west1"),
)
gds.verify_connectivity()

Mode C — Standalone (no Neo4j DB):

gds = sessions.get_or_create(
    session_name="my-standalone",
    memory=SessionMemory.m_4GB,
    ttl=timedelta(hours=1),
    cloud_location=CloudLocation("gcp", "europe-west1"),
)
gds.verify_connectivity()

get_or_create() is idempotent; reconnects to existing session by name.

Step 4 — Project Graph

From connected Neo4j (remote projection):

query = """
    CALL () {
        MATCH (p:Person)
        OPTIONAL MATCH (p)-[r:KNOWS]->(p2:Person)
        RETURN p AS source, r AS rel, p2 AS target,
               p {.age, .score} AS sourceNodeProperties,
               p2 {.age, .score} AS targetNodeProperties
    }
    RETURN gds.graph.project.remote(source, target, {
        sourceNodeLabels:     labels(source),
        targetNodeLabels:     labels(target),
        sourceNodeProperties: sourceNodeProperties,
        targetNodeProperties: targetNodeProperties,
        relationshipType:     type(rel)
    })
"""

G, result = gds.graph.project.cypher(
    graph_name="my-graph",
    query=query,
    undirected_relationship_types=["KNOWS"],
)
print(f"Projected {G.node_count()} nodes, {G.relationship_count()} relationships")

CALL () { ... } required for multi-pattern MATCH. Use UNION inside CALL for multiple labels/rel types. Remote query must use gds.graph.project.remote(...); graph name goes to gds.graph.project.cypher(...), not the query. Query containing gds.graph.project without .remote is auto-rewritten with a warning. undirectedRelationshipTypes / inverseIndexedRelationshipTypes inside the query → ValueError — pass as method args. Only numeric node properties can be projected into a session; fetch string properties via db_node_properties when streaming. Standalone sessions cannot remote-project — ValueError: Remote projection is only supported for attached Sessions. 1.x fallback: gds.graph.project(graph_name=..., query=...).

Native remote projection (no Cypher query) — gds.graph.project.native(...) projects from the attached DB by label/type filter:

G, result = gds.graph.project.native(
    "my-graph",
    ["Person"],                              # node_label_filter
    ["KNOWS"],                               # relationship_type_filter
    node_properties=["age", "score"],
    undirected_relationship_types=["KNOWS"],
)

Attached sessions only. Use project.native for label/type-filtered projections; use project.cypher for transformations, computed properties, or UNION heterogeneous patterns.

AuraDB Cypher API projection:

CYPHER runtime=parallel
MATCH (source)
OPTIONAL MATCH (source)-->(target)
RETURN gds.graph.project(
  'my-graph',
  source,
  target,
  {},
  { memory: '2GB' }
)

Existing explicit session:

CYPHER runtime=parallel
MATCH (source)
OPTIONAL MATCH (source)-->(target)
RETURN gds.graph.project(
  'my-graph',
  source,
  target,
  {},
  { sessionId: '00000000-11111111' }
)

Cypher API uses gds.graph.project(...), not gds.graph.project.remote(...). Put memory, ttl, sessionId, batchSize in fifth config argument.

Session management via Cypher API:

CALL gds.session.getOrCreate('test-session', '2GB', duration({minutes: 30}))
YIELD id, name, status
RETURN id, name, status

CALL gds.session.list()
YIELD id, name, status, memory
RETURN id, name, status, memory

Implicit Cypher API sessions delete when all projected graphs in session are dropped.

From Pandas DataFrames (standalone mode):

import pandas as pd

nodes_df = pd.DataFrame([
    {"nodeId": 0, "labels": "Person", "age": 30},
    {"nodeId": 1, "labels": "Person", "age": 25},
])
rels_df = pd.DataFrame([
    {"sourceNodeId": 0, "targetNodeId": 1, "relationshipType": "KNOWS"},
])

G = gds.graph.construct("my-graph", [nodes_df], [rels_df])

Required columns — nodes: nodeId (int), labels (str). Relationships: sourceNodeId, targetNodeId, relationshipType. Drop string node properties before construct() — sessions accept numeric properties only.

Step 5 — Run Algorithms

# Mutate — chain results without writing to DB
gds.page_rank.mutate(G, mutate_property="pagerank", damping_factor=0.85)
gds.fast_rp.mutate(G,
    mutate_property="embedding",
    embedding_dimension=128,
    feature_properties=["pagerank"],
    random_seed=42,
)

# Stream — inspect results as DataFrame
df = gds.page_rank.stream(G)
print(df.sort_values("score", ascending=False).head(10))

# Write — persist to connected Neo4j DB (connected modes only)
gds.louvain.write(G, write_property="community")

ML pipelines: gds.pipeline.node_classification / link_prediction / node_regression — the only API in 2.0. 1.x fallback: gds.v2.page_rank.mutate(...); untyped 1.x endpoints like gds.pageRank.mutate(...) are gone in 2.0. Plugin algorithm reference → neo4j-gds-skill; AGA limitations differ.

Step 6 — Async Job Polling

Long-running algorithms — non-blocking compute() returns a JobHandle:

import time

job = gds.page_rank.compute(G, damping_factor=0.85)   # algorithm config only — no mutate/write args
while not job.done():
    time.sleep(5)
    print(f"Job status: {job.status().status} {job.status().progress_percent()}")
if not job.status().succeeded():                       # status string "done" / "aborted" / "running"
    raise RuntimeError(f"Algorithm job failed: {job.status().status}")
df = job.stream(wait=False)                            # raises JobNotFinishedError if not done
# or: job.mutate(mutate_property="pagerank")  /  job.write(write_properties="pagerank") → WriteJobHandle

Handle methods: .job_id(), .status() → JobStatus (.status, .progress, .succeeded(), .aborted(), .running()), .done(), .wait(*, termination_flag=None), .cancel(), .summary(wait=True), .stream(wait=True), .mutate(mutate_property=..., wait=True), .write(write_properties=...).

GraphSAGE prediction as job [2.2]: gds.graph_sage.compute(G, model_name) (classic model), gds.graph_sage.supervised.compute(G, model_name, feature_properties) (adds predicted_class, predicted_probabilities), gds.graph_sage.unsupervised.compute(G, model_name, feature_properties) — each returns JobHandle; materialize via .stream() / .mutate() / .write(). Async projections return ProjectionJobHandle (gds.graph.project.native_async(...), cypher_async(...)); write-backs yield WriteJobHandle. List/recover jobs:

gds.jobs.list()                     # JobInfo per job: job_id, name
handle = gds.jobs.get(G, job_id)    # concrete handle type for the job

Step 7 — Retrieve Results

# Stream node properties
result_df = gds.graph.node_properties.stream(
    G,
    node_properties=["pagerank", "embedding"],
    db_node_properties=["name"],   # connected modes only — fetches string props from DB
)
result_df.head(10)

Standalone mode: no db_node_properties; join source DataFrame:

result_df = gds.graph.node_properties.stream(G, ["pagerank"])
result_df.merge(nodes_df[["nodeId", "name"]], how="left")

Step 8 — Write Back and Clean Up

# Write node properties to connected Neo4j
gds.graph.node_properties.write(G, ["pagerank", "embedding"])

# Write relationship properties
gds.graph.relationships.write(G, "SIMILAR", ["score"])

# Query connected DB from session
gds.run_cypher("MATCH (n:Person) RETURN count(n)")

# Drop projected graph
gds.graph.drop(G)

# Delete session
sessions.delete(session_name="my-analysis")
# or: gds.delete()

Write before delete; unwritten results lost when session closes.

Session Management

# List active sessions
from pandas import DataFrame
DataFrame(sessions.list())

# Reconnect to existing session
gds = sessions.get_or_create(session_name="my-analysis", memory=..., db_connection=...)

Common Errors

References

Load on demand:

• references/workflows.md — full AuraDB and standalone workflow examples, Spark integration
• references/limitations.md — AGA vs embedded GDS feature table, SessionMemory tiers, cloud locations

WebFetch

Checklist

• Aura API credentials created and set in environment (AURA_CLIENT_ID, AURA_CLIENT_SECRET)
• Connected sessions: AURA_INSTANCEID or NEO4J_URI, plus NEO4J_USERNAME, NEO4J_PASSWORD set for DbmsConnectionInfo.from_env()
• AGA feature enabled for Aura project (Aura Console → project settings)
• Memory estimated before session creation (sessions.estimate(..., algorithms=[...]))
• Cloud location chosen near data source
• gds.verify_connectivity() called after session creation
• Remote projection uses gds.graph.project.cypher(graph_name, query) with gds.graph.project.remote(...) inside query
• Remote projection graph name passed to the endpoint, not the remote function
• undirected_relationship_types passed as method args, never inside the query
• AuraDB Cypher API projection uses fifth config map for memory or sessionId
• Explicit Cypher API sessions use gds.session.getOrCreate(...); implicit sessions dropped with projected graph
• TTL set to avoid unexpected costs on idle sessions
• Async algorithm jobs polled until RUNNING_DONE before reading results
• Results written back (connected modes) or streamed and persisted (standalone) before deletion
• Session deleted when done (sessions.delete(session_name=...) or gds.delete())