This is a review of Fraud Detection methods based on graph algorithms, graph databases, machine learning, and graph neural networks on NebulaGraph, and in addition to an introduction to the basic methodological ideas, I’ve also got a Playground you can run. it’s worth mentioning that this is the first time I’ve introduced you to the Nebula-DGL project 😁.
Do I have to create my own graph model and everything to set up a Data Lineage system? Thanks to many great open-source projects, the answer is: No!
Today, I would like to share my opinionated reference data infra stack with some of those best open-source projects with modern ETL, Dashboard, Metadata Governance, and Data Lineage Management.
What could be done with Spark and PySpark on top of Nebula Graph, this post covers everything we should know.
With the ARM64 Docker Image of Nebula Graph, it’s actually quite easy to run it on SBC/Respberry Pi!
Could I create something between the human brain and the game-cheater/ruiner to make it more of fun? With Knowledge Graph?
How to parse nebula graph data in an interactive way and what are the best practices?
I will show you an easier way in this article 😁.
