Neo4j Reference

This section documents the current Neo4j 2026.x calendar-versioned line — Neo4j moved from semantic to calendar versioning (YYYY.MM) in 2025, and the same line applies to the Graph Data Science library — as published at the Neo4j documentation, which is the reference these pages are written and verified against. No specific monthly patch is pinned. Some areas (Aura’s internal infrastructure, the Raft consensus implementation details, and the GDS Pregel API’s low-level internals) are linked, not documented in depth.

This content was generated with the assistance of AI and should be verified against the official documentation before being relied on in production, as Neo4j iterates quickly.

This section’s bibliography lists the reference material consulted while preparing these pages.

Welcome to the Neo4j reference. Neo4j is a native graph database: it stores data as a property graph — nodes and relationships, each carrying properties — and traverses it by index-free adjacency, querying it through Cypher, its declarative graph query language. This section documents Neo4j as a database-developer reference — the property graph model, Cypher from fundamentals through advanced querying, data modeling, importing data, indexes and constraints, transactions and the driver family, APOC, security and administration, clustering and multi-database, the Graph Data Science (GDS) library, vector search and GraphRAG, and worked use cases — written and verified against the Neo4j documentation.

If you are new to Neo4j, read Getting Started first, then the property graph model, Cypher fundamentals, advanced Cypher querying and data modeling. Everything after that builds on those foundations.

For the document-database and distributed-JSON baselines this section contrasts with, see the sibling MongoDB Reference and Couchbase Reference, and see Choosing the Right Database for when a graph database is the right tool in the first place.

What’s covered

Foundations

Getting Started — editions, deployment options (Neo4j Desktop, Docker, self-managed, Aura), Neo4j Browser and cypher-shell, and a first end-to-end round trip.
The Property Graph Model — nodes, relationships, labels, properties, and the schema-optional data model.
Cypher Fundamentals — pattern syntax, CREATE/MATCH/MERGE/ WHERE/RETURN/SET/REMOVE/DELETE, and query parameters.
Cypher: Advanced Querying — query chaining with WITH, CALL \{ \} subqueries, UNION, comprehensions, UNWIND, OPTIONAL MATCH, variable-length paths and shortest-path functions.
Data Modeling — the modeling process, node vs. relationship vs. property decisions, relational-to-graph translation, supernode anti-patterns, and versioned/temporal modeling.

Operating a Neo4j database

Importing Data — LOAD CSV with batched transactions, neo4j-admin bulk import, the Data Importer tool, and APOC-based loading.
Indexes & Constraints — range, text, point, composite, full-text and vector indexes, and uniqueness/existence/node-key/relationship constraints.
Transactions & Drivers — ACID guarantees, causal consistency and bookmarks, and the Bolt driver family’s transaction-function pattern.
APOC & Extensions — installing and allowlisting APOC, refactoring, loading, path-utility and export procedures, and the wider extension ecosystem.
Security & Administration — authentication, role-based access control and privilege statements, multi-database basics, backup/restore, and Bolt+TLS.
Clustering & Multi-Database — Raft-based clustering, primary/secondary server roles, multi-database and composite databases, and Aura’s managed clustering.

Graph Data Science

Graph Data Science Fundamentals — the GDS plugin and in-memory graph catalog, native vs. Cypher projections, execution modes, and an algorithm-category overview.
Pathfinding & Centrality Algorithms — Dijkstra, A*, Yen’s k-shortest paths, Degree, PageRank, Betweenness, Closeness and Eigenvector centrality.
Community Detection Algorithms — WCC, SCC, Triangle Count, the Local Clustering Coefficient, Louvain, Label Propagation and Leiden.
Similarity, Embeddings & ML Pipelines — Node Similarity, KNN, FastRP, Node2Vec, GraphSAGE, and the node-classification and link-prediction pipelines.
Vector Search & GenAI — the native vector type and vector indexes, the Cypher SEARCH clause, GraphRAG, and the neo4j-graphrag-python/LangChain4j/Spring AI integrations.

Applying it

Use Cases & Algorithms in Practice — recommendation engines, fraud and AML ring detection, routing and logistics optimization, identity and access graphs, and root-cause and dependency analysis.
Neo4j vs. Relational & Other Graph Databases — index-free adjacency vs. joins, positioning against other graph databases and RDF/triple stores, and when to reach for Neo4j specifically.

Cheat sheet

Cheat Sheet (PDF) — a single-page, printable summary of everything in this section, with a downloadable PDF.

Bibliography

The Neo4j documentation — the source every page in this section is written and verified against, covering the current Neo4j 2026.x calendar-versioned line (calendar-versioned, YYYY.MM, since 2025; the same line applies to the Graph Data Science library); see in particular Getting Started, Cypher Manual, Operations Manual, Java driver manual, Python driver manual, APOC docs, GDS docs, Aura docs, and neo4j-graphrag-python docs.
The Neo4j GenAI ecosystem and the vector-search developer guide — the GraphRAG and vector-search material referenced from Vector Search & GenAI.
Neo4j’s graph vs. relational comparison and this overview — already cited in Choosing the Right Database, reused rather than re-sourced.
Eastridge, Timothy. Graph Data Science with Python and Neo4j. Orange Education (AVA), 2024. ISBN 978-81-97081-96-5. Consulted as part of the bibliography for this section — see the publisher’s book page.
Scifo, Estelle. Graph Data Science with Neo4j. Packt Publishing, 2023. ISBN 978-1-80461-274-3. Consulted as part of the bibliography for this section — see the publisher’s book page.
Hutson, Gary; Jackson, Matt. Graph Data Modeling in Python. Packt Publishing, 2023. ISBN 978-1-80461-803-5. Consulted as part of the bibliography for this section — see the publisher’s book page.
Bratanič, Tomaž. Graph Algorithms for Data Science. Manning Publications, 2024. ISBN 978-1-61729-946-9. Consulted as part of the bibliography for this section — see the publisher’s book page.
Anthapu, Ravindranatha; Agarwal, Siddhant. Building Neo4j-Powered Applications with LLMs. Packt Publishing, 2025. ISBN 978-1-83620-623-1. Consulted as part of the bibliography for this section — see the publisher’s book page.
Barrasa, Jesús; Webber, Jim. Building Knowledge Graphs: A Practitioner’s Guide. O’Reilly Media, 2023. ISBN 978-1-09812-710-7. Consulted as part of the bibliography for this section — see the publisher’s book page.
The six books above are consulted bibliographic references only. They are not the primary or main reference for this section, so where they and neo4j.com/docs disagree, the official documentation is authoritative and the difference is noted.