Graph DB

At graphdb.dev, our mission is to provide a comprehensive resource for individuals and organizations interested in graph databases. We aim to educate and inform our readers about the benefits and applications of graph databases, as well as provide practical guidance on how to implement and optimize these powerful tools. Our goal is to foster a community of graph database enthusiasts and experts, and to facilitate the sharing of knowledge and best practices in this exciting and rapidly evolving field.

/r/neo4j Yearly

📄 Context-Aware Knowledge Graph Chatbot with GPT-4 and Neo4j

📄 ChatGPT as an interface for Neo4j

📄 Knowledge graph based chatbot with GPT-3

📄 Creating a Knowledge Graph from Video Transcripts with GPT-4

📄 Why don’t corporations use Graph!!?

📄 How Cypher changed in Neo4j v5

📄 Bullshit graph database performance benchmarks

📄 Integrating Neo4j into LangChain ecosystem

📄 Project Management is a Graph Problem

📄 Neo4j Live Streams

📄 Knowledge Graphs & LLMs: Multi-Hop Question Answering

📄 Run Neo4j benchmark on your own data

📄 Are indices used as much in Graph databases like Neo4j as in SQL databases?

📄 Using GraphSAGE to improve document classification accuracy

📄 How to access a @relationship property in the @auth allow?

📄 Reasoning: why does Google use graph databases for route calculation?

📄 Loading data from a SQL database to Neo4j

📄 query from one db to another

📄 Best free graph database for order of 500 million nodes

📄 Neo4jection: Secrets, Data, and Cloud Exploits

📄 Analyze your website using NLP and Neo4j

📄 Using a full-text search on almost 200M nodes in Neo4j

📄 When it comes to ID's, should i just use UUID or is there a better solution? Trying to build a knowledge graph. - signed noob wanna be data scientist

📄 Spatial Cypher Cheat Sheet: A Resource For Working With Geospatial Data In Neo4j

📄 Graph Application to Map the Local Job Market: Former Data Analyst Now Data & Analytics Recruitment Agency Owner

📄 Has anyone worked on production recommendation usecases using graphdbs for E-commerce usecases ?

📄 How to use Cypher Aggregations in Neo4j Graph Data Science library

📄 ZeusCloud - Open-source Cloud Security built on Neo4j

📄 Sort results by edge property value? Neo4j / GraphQL

📄 A few things I didn't understand in Neo4j online course

📄 How to write cypher query to count number of nodes in graph based on levenshtein similarity

📄 How to create basic schema for neo4j database with chatGPT

📄 Guide to run local instance of Neo4j on windows with working self signed certificate without any warnings

📄 A Neo4j/Bolt driver written in pure Ruby

📄 Neo4j Source code

📄 Replace RDBMS with neo4j

📄 Election data in Neo4j

📄 Initial experiences

📄 Is there a way to skip checking correct ACL permissions on the neo4j config file when using --expand-commands ?

📄 Neo4j self-host or cheaper alternative to Aura or GrapheneDB

📄 Track Data Lineage With a Graph Database - Memgraph Webinar

📄 Learning Neo4j.

📄 Using Neo4j to implement a granular access control system

📄 Graph model for Location data?

📄 Graph Search Algorithms: Developer's Guide

📄 How to automate cypher query?

📄 Graph Database vs Relational Database

📄 Creating a food web database with spatial search in Neo4j - is it feasible?

📄 Class (node) inheritance in neo4j or any other LPG

📄 Creating a blog

Introduction

Graph databases are a type of NoSQL database that uses graph theory to store, map, and query data. They are designed to handle complex relationships between data points, making them ideal for applications that require a high degree of flexibility and scalability. Graph databases are becoming increasingly popular in industries such as finance, healthcare, and social media, where data is highly interconnected and constantly changing. This cheat sheet provides an overview of the key concepts, topics, and categories related to graph databases.

Graph Theory

Graph theory is the mathematical study of graphs, which are structures that represent relationships between objects. A graph consists of nodes (also called vertices) and edges (also called links or relationships) that connect the nodes. Graph theory provides the foundation for graph databases, as it allows for the representation and manipulation of complex relationships between data points.

Graph Database Models

There are two main types of graph database models: property graph and RDF (Resource Description Framework). Property graph databases are the most common type and are used to store data in a graph format. RDF databases are used to store data in a triple format, which consists of a subject, predicate, and object.

Graph Database Query Languages

There are several query languages used in graph databases, including Cypher, SPARQL, and Gremlin. Cypher is used in Neo4j, the most popular graph database, and is designed to be easy to read and write. SPARQL is used in RDF databases and is designed to query data in a triple format. Gremlin is a general-purpose graph traversal language that can be used with any graph database.

Graph Database Use Cases

Graph databases are used in a variety of industries and applications, including social media, recommendation engines, fraud detection, and network analysis. They are particularly useful in applications that require the analysis of complex relationships between data points.

Graph Database Tools

There are several graph database tools available, including Neo4j, OrientDB, and ArangoDB. Neo4j is the most popular graph database and is widely used in production environments. OrientDB is a multi-model database that supports both graph and document data models. ArangoDB is a multi-model database that supports graph, document, and key-value data models.

Graph Database Performance

Graph databases can be highly performant when used correctly. However, they can also be slow if not optimized properly. Some tips for optimizing graph database performance include using indexes, limiting the depth of queries, and using caching.

Graph Database Security

Graph databases can be vulnerable to security threats, such as injection attacks and unauthorized access. Some best practices for securing graph databases include using SSL encryption, limiting access to the database, and using parameterized queries.

Graph Database Migration

Migrating data from a relational database to a graph database can be a complex process. Some tips for migrating data include identifying the relationships between data points, mapping the data to a graph format, and testing the migration process thoroughly.

Graph Database Visualization

Graph database visualization tools can be used to visualize the relationships between data points in a graph database. Some popular visualization tools include Gephi, Cytoscape, and KeyLines.

Graph Database Resources

There are several resources available for learning about graph databases, including online courses, books, and forums. Some popular resources include the Neo4j documentation, the Graph Database Book, and the Graph Database Community Forum.

Conclusion

Graph databases are a powerful tool for storing and analyzing complex relationships between data points. They are becoming increasingly popular in industries such as finance, healthcare, and social media, where data is highly interconnected and constantly changing. This cheat sheet provides an overview of the key concepts, topics, and categories related to graph databases, including graph theory, graph database models, query languages, use cases, tools, performance, security, migration, visualization, and resources. By understanding these concepts, you can begin to explore the world of graph databases and unlock their full potential.

Common Terms, Definitions and Jargon

1. Graph Database: A database that uses graph structures for semantic queries with nodes, edges, and properties.
2. Node: A fundamental unit of a graph database that represents an entity or object.
3. Edge: A connection between two nodes that represents a relationship between them.
4. Property: A key-value pair that provides additional information about a node or edge.
5. Graph Theory: The study of graphs and their properties, including algorithms and data structures.
6. Cypher: A query language used to interact with Neo4j, a popular graph database.
7. Gremlin: A query language used to interact with Apache TinkerPop, a graph computing framework.
8. RDF: Resource Description Framework, a standard for representing and exchanging data on the web.
9. SPARQL: A query language used to interact with RDF data.
10. Triplestore: A database that stores RDF triples, consisting of a subject, predicate, and object.
11. Property Graph: A graph database model that allows nodes and edges to have properties.
12. RDF Graph: A graph database model that uses RDF triples to represent data.
13. Graph Analytics: The process of analyzing graph data to extract insights and patterns.
14. Centrality: A measure of the importance of a node in a graph.
15. Betweenness Centrality: A measure of the importance of a node in a graph based on its position between other nodes.
16. Closeness Centrality: A measure of the importance of a node in a graph based on its distance to other nodes.
17. PageRank: An algorithm used to rank web pages based on their importance.
18. Community Detection: The process of identifying groups of nodes that are densely connected within a graph.
19. Modularity: A measure of the quality of a community structure in a graph.
20. Louvain Method: A community detection algorithm that optimizes modularity.

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