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
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.
(more…)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.
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.
(more…)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
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.
(more…)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.
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 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.
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