Graph Databases: Neo4j, RDF, Knowledge Graphs & GraphRAG

Explore key graph concepts: degree of nodes, tree and dag structures, bipartite and multigraphs, and weighted graphs with multiple weights, with examples like file systems, family trees, and ETL pipelines.

Explore how graph databases store data as nodes and edges from road networks, and compare property graphs like Neo4j with RDF triple stores, using Cypher and Dijkstra for shortest paths.

Explore deleting nodes and edges in Neo4j using detach delete to remove a node and its relationships, and delete to remove an edge, with examples of directed and undirected graphs.

Neo4j indexing speeds lookups by creating an index on a property (like email). The query planner uses the index to locate the node by its ID quickly.

Install python and visual studio code on windows, with mac steps similar. Download python 3.13.7, download vs code, install, and create a desktop icon before you start coding in python.

Explore Python data structures: lists, tuples, sets, and dictionaries—and learn to store game state and a three-user scoreboard. Use while and for loops to run the rounds.

Refactor and streamline Python code by using the def keyword to create methods like generate_random_number and verify_number, improving readability, maintainability, and reuse.

Demonstrate Python multiprocessing to accelerate cpu-intensive tasks by using pool.map across four processes, fetching and parsing web content with requests and BeautifulSoup.

Install Python on Windows and set up Visual Studio Code, then connect Python with Neo4j to move from Neo4j's desktop Cypher testing to production interactions.

Create a requirements.txt file with the necessary python packages for working with neo4j, activate the virtual environment, and install them using pip -r requirements.txt.

Generate dummy data with a Python faker-like package, write it to a friends.csv file, then load the CSV to create person nodes and friendship relationships in Neo4j.

Apply the DFS algorithm to enumerate all paths from A to F and write Cypher queries to create nodes and edges and detect cycles with root star.

Explore how breadth first search traverses a graph by exploring each node's neighbors before moving deeper, revealing shortest paths, nearest locations, and social connections using a queue-based traversal.

Explore RDF, the resource description framework, and how it represents information as triples—subject, predicate, and object—enabling data integration across silos and linked open data.

Install GraphDB on Windows, obtain a free license, and access the localhost 7200 interface to upload RDF, run turtle-based RDF snippets, and manage RDF data.

Create rdf triples for John and Alice linking government, purchase, and medical records. Demonstrate creating csv, python etl, and importing into graph db for sparql queries.