Summary

Keywords

neo4j graph databases graphs LLM retrieval augmented generation RAG vector database vector search GenAI AI Artificial intelligence nodes25

Full Transcript

As a data scientist with a background in computer vision and generative models, Suha has spent years working with embeddings. neural networks, and structured pipelines; however, graph databases and graph query languages were completely new territory. In this talk, she will share what it was like to explore graph thinking and graph query languages (GQLs) for the first time, using a public face recognition dataset (like CelebA-HQ) to model relationships between people, facial attributes, and features. Suha will walk through how she translated tabular and image-based data into a graph structure, experimented with different graph tools and GQLs, and ran queries to surface patterns and insights not easily seen in flat data. Along the way, she will reflect on how this experience shifted her mental model of data relationships and enriched her understanding of knowledge graphs, especially in the context of ML. This talk is for anyone curious about graph technologies but unsure of where to begin. Whether you come with a SQL, Python, or ML background, you will leave with a clear, practical roadmap for starting your own graph-powered project. Speaker: Suha Mokalla Resources: Get Started with Aura - https://bit.ly/3LOLrjh Deployment Center - https://bit.ly/4jOelM3 Ground AI Systems and Agents with Neo4j - https://bit.ly/4oVsnyb #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph