This Week in Neo4j: Vectors, Knowledge Graphs, Clustering, Social Networks and more

Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
At Microsoft Fabric, we introduced the native integration with MS Fabric as well as Azure OpenAI Service improving data management, GenAI results as well as reduced AI hallucinations – Watch it in Action.

Besides this exciting announcement, this edition features two great new courses about Vector Indexes and RAG with Knowledge Graphs – articles on Graph Clustering and Building a Social Network round us off.

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What’s in it for you? A chance to connect directly with product development teams, get paid compensation, hear about what we are working on, and more!

I hope you enjoy this issue,
Alexander Erdl


Leann Chen is a Content creator / Video Editor and Coder. She is passionate about knowledge graphs & generative AI, and she has recently shared a lot of exciting content on these topics.
Connect with her on LinkedIn.

In a livestream “Powering Advanced Streamlit Chatbots with GenAI” we discussed how RAG and Neo4j are taking AI chatbots to the next level. The session demonstrated using Langchain, Neo4j, and Streamlit to translate unstructured data into clear, visual narratives.

Leann Chen
GRAPHACADEMY: Introduction to Vector Indexes and Unstructured Data
This brand-new course provides comprehensive training on processing and understanding unstructured data using Neo4j and vector indexes, covering dataset exploration, embedding creation, and graph database construction with Python, LangChain, and OpenAI.
DEEPLEARNING: Knowledge Graphs for RAG
This course teaches the creation and application of knowledge graphs to structure complex data, enhance AI applications through intelligent search and reasoning, and improve large language models by providing structured, relevant context using Neo4j, Cypher, and vector indexes.
CLUSTERING: Clustering Large Graphs With CLARANS
CLARANS was developed to extend k-medoids to larger datasets than were practical with earlier k-medoid algorithms. The CLARANS algorithm functions like a web crawler navigating a graph, iteratively moving to neighboring nodes with lower scores. Nathan Smith shows us how to use it for medium to large graphs.
SOCIAL NETWORK: Social Seed – Build Your Own Social Network
Social Seed by Dairon Pérez Frías provides a solid starting point for creating a personalised social network using the powerful combination of Spring Boot for the backend, Neo4j as the graph database, and Vue.js for the front-end.


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