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

‘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

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Reintroducing the ArangoDB-RDF Adapter

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

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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What is a Graph Database?

Estimated reading time: 10 minutes

Introduction

Graphs occur everywhere in everyday life: your network of friends, the network of roads you drive on, and the supply chain of factories, ships, and roads that brought you the device you’re reading this on. While it might be easy to connect the dots on how most things can be shown as a graph, what makes a database a graph database? That is the question you will have the answer to in this blog post, but to put it simply: a graph consists of nodes, edges, and properties representing the relationships within data.

In this article, we will discuss:

  • What is a graph?
  • What is a graph database?
  • Different types of graph databases.
  • Graph database use cases.
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recommendMovies

Integrate ArangoDB with PyTorch Geometric to Build Recommendation Systems

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.

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scatter plot graphsage

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

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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Blog Post Template

Community Notebook Challenge

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.

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Detecting Complex Fraud Patterns with ArangoDB

Detecting Complex Fraud Patterns with ArangoDB

Introduction

This article presents a case study of using AQL queries for detecting complex money laundering and financial crime patterns. While there have been multiple publications about the advantages of graph databases for fraud detection use cases, few of them provide concrete examples of implementing detection of complex fraud patterns that would work in real-world scenarios. 

This case study is based on a third-party transaction data generator, which is designed to simulate realistic transaction graphs of any size. The generator disguises complex financial fraud patterns of two kinds: 

  • Circular money flows: a big amount of money is going through different nodes and comes back to the source node.
  • Indirect money transfers: a big amount of money is sent from source node to a target node over a multi-layered network of intermediate accounts.
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ArangoML Series: Multi-Model Collaboration

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.
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning
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ArangoML Series: Intro to NetworkX Adapter

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 here to better fit the blog post format and provide a quick introduction to using the NetworkX adapter. 

ArangoML Pipeline Cloud graphic showing an example machine learning pipeline
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