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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
    (more…)

    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: 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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    Best Practices for AQL Graph Queries

    May 27, 2020

    Tags: Query Language, how to, Graphs, General

    Estimated reading time: 9 minutes

    Estimated reading time: 8 minutes

    The ArangoDB Query Language(AQL) was designed to accomplish a few important goals, including:

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    Performance analysis with pyArango: Part III Measuring possible capacity with usage Scenarios

    October 26, 2017

    Tags: Community, Performance, how to, General, monitoring, python, testing, Tutorial

    Estimated reading time: 14 minutes

    So you measured and tuned your system like described in the Part I and Part II of these blog post series. Now you want to get some figures how many end users your system will be able to serve. Therefore you define “scenarios” which will be typical for what your users do. One such a user scenario could i.e. be:

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    Setting up Datacenter to Datacenter Replication in ArangoDB

    October 12, 2017

    Tags: Cluster, Enterprise, Architecture, Releases, Replication, Performance, how to, General

    Estimated reading time: 7 minutes

    Please note that this tutorial is valid for the ArangoDB 3.3 milestone 1 version of DC to DC replication!

    Interested in trying out ArangoDB? Fire up your cluster in just a few clicks with ArangoDB ArangoGraph: the Cloud Service for ArangoDB. Start your free 14-day trial here

    This milestone release contains data-center to data-center replication as an enterprise feature. This is a preview of the upcoming 3.3 release and is not considered production-ready.

    In order to prepare for a major disaster, you can setup a backup data center that will take over operations if the primary data center goes..

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    How to put ArangoDB to Spartan-Mode

    March 08, 2016

    Tags: Foxx, how to, General, Tutorial

    Estimated reading time: 3 minutes

    Most of us saw the fantastic movie 300 (I did it last night…again) or at least read the comics. 300 spartans barely wearing anything but achieving a lot. This little how-to will show you how to put ArangoDB into Spartan-Mode and thereby reduce memory-footprint and CPU usage.

    Big thanks to Conrad from L.A. for his time and for giving us the impulse for this little how-to!

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