ArangoDB Blog | Graphs

The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB

February 07, 2024

Estimated reading time: 5 minutes

‘Guest Blog’

Estimated reading time: 5 minutes

In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ

The original problem in cloud security is visibility into my assets. If security engineers donʼt know what cloud services are running, they canʼt protect an environment. Unfortunately, first generation cloud security products were built with a list mindset, i.e. “rows and columnsˮ. They generate a list of assets and their configurations – but show no context of..

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Reintroducing the ArangoDB-RDF Adapter

January 31, 2024

Tags: turtle, Data Science, semantic web, adapter, Graph, Graphs, RDF, resource description framework

Estimated reading time: 2 minutes

Estimated reading time: 1 minute

ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.

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Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience

December 14, 2023

Tags: Graph DB, Data Loader, — Relational Database, Graph, Graphs, Relational Database, graph database, Graph DBs, GraphDB, Migrate to Graph

Estimated reading time: 7 minutes

At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.

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Introducing the ArangoDB-PyG Adapter

August 04, 2022

Tags: ArangoML, Machine Learning, how to, Graphs, General, Graph Neural Networks, PyG, pytorch, PyTorch Geometric, torch

Estimated reading time: 1 minutes

Estimated reading time: 10 minutes

We are proud to announce the GA 1.0 release of the ArangoDB-PyG Adapter!

The ArangoDB-PyG Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into PyTorch Geometric (PyG), a PyTorch-based Graph Neural Network library, and vice-versa.

On July 29 2022, we introduced the first release of the PyTorch Geometric Adapter to the ArangoML community. We are proud to have PyG as the fourth member of our ArangoDB Adapter Family. You can expect the same developer-friendly adapter options and a helpful getting-started guide via Jupyter..

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Integrate ArangoDB with PyTorch Geometric to Build Recommendation Systems

April 06, 2022

Tags: ArangoML, Machine Learning, Graph, how to, Graphs, General, recommendation, pytorch

Estimated reading time: 1 minutes

Estimated reading time: 20 minutes

In this blog post, we will build a complete movie recommendation application using ArangoDB and PyTorch Geometric. We will tackle the challenge of building a movie recommendation application by transforming it into the task of link prediction. Our goal is to predict missing links between a user and the movies they have not watched yet.

Run this notebook yourself: https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Integrate_ArangoDB_with_PyG.ipynb

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A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark

August 13, 2021

Tags: ArangoML, Machine Learning, Graph, how to, Graphs, General, graphsage, python, pytorch, ML

Estimated reading time: 1 minutes

Estimated reading time: 15 minute

This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:

What is GraphSage
Neighbourhood Sampling
Getting Hands-on Experience with GraphSage and PyTorch Geometric Library
Open-Graph-Benchmark’s Amazon Product Recommendation Dataset
Creating and Saving a model
Generating Graph Embeddings Visualizations and Observations

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Community Notebook Challenge

August 13, 2021

Tags: Fraud Detection, AQL, Query Language, Graph, how to, Graphs, General

Estimated reading time: 2 minutes

Calling all Community Members! 🥑

Today we are excited to announce our Community Notebook Challenge.

What is our Notebook Challenge you ask? Well, this blog post is going to catch you up to speed and get you excited to participate and have the chance to win the grand prize: a pair of custom Apple Airpod Pros.

Our Interactive Tutorials repository has a library of python notebooks available covering the full spectrum of ArangoDB features. Although we cover a lot of topics, something is missing… YOUR notebook! As announced in our July Newsletter on July 29th and running until October 31st, the ..

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ArangoML Series: Multi-Model Collaboration

January 26, 2021

Tags: ArangoML, Machine Learning, Graph, Graphs, General, python

Estimated reading time: 9 minutes

Estimated reading time: 8 minutes

Multi-Model Machine Learning

This article looks at how a team collaborating on a real-world machine learning project benefits from using a multi-model database for capturing ML meta-data.

The specific points discussed in this article are how:

The graph data model is superior to relational for ML meta-data storage.
Storing ML experiment objects is natural with multi-model.
ArangoML promotes collaboration due to the flexibility of multi-model.
ArangoML provides ops logging and performance analysis.

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ArangoML Series: Intro to NetworkX Adapter

November 17, 2020

Tags: ArangoML, arangopipe, Machine Learning, Graph, how to, Graphs, General, networkx, python

Estimated reading time: 4 minutes

Estimated reading time: 3 minutes

This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX.

In this post we:

Briefly introduce NetworkX
Explore the IMDB user rating dataset
Showcase the ArangoDB integration of NetworkX
Explore the centrality measures of the data using NetworkX
Store the experiment with arangopipe

This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter repository. It is summarized..

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ArangoML Part 4: Detecting Covariate Shift in Datasets

November 05, 2020

Tags: ArangoML, Machine Learning, Graphs, General

Estimated reading time: 2 minutes

Estimated reading time: 1 minute

This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more.

In this post we:

Introduce the concept of covariate shift in datasets
Showcase the built-in dataset shift detection API

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The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB

Reintroducing the ArangoDB-RDF Adapter

Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience

Introducing the ArangoDB-PyG Adapter

Integrate ArangoDB with PyTorch Geometric to Build Recommendation Systems

A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark

Community Notebook Challenge

ArangoML Series: Multi-Model Collaboration

ArangoML Series: Intro to NetworkX Adapter

ArangoML Part 4: Detecting Covariate Shift in Datasets

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