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How ArangGraphML Leverages Intel’s PyG Optimizations
Estimated reading time: 3 minutes
ArangoGraphML + Intel: Next-level Machine Learning Accelerated
ArangoDB and Intel have announced a groundbreaking partnership to enhance Graph Machine Learning (GraphML) using Intel’s high-performance processors. This collaboration, part of the Intel Disruptor Program, will seek to integrate ArangoDB’s graph database solutions with Intel’s Xeon CPU. This synergy promises to revolutionize data analytics and pattern recognition in complex graph structures, marking a new era in database technology and GraphML advancements.
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Introducing the ArangoDB-PyG Adapter
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 Notebook, and stay tuned for an upcoming Lunch & Learn session!
This blog post will serve as a walkthrough of the ArangoDB-PyG Adapter, via its official Jupyter Notebook.
(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.
Who’s Who in Data Science
Estimated reading time: 10 minutes
Multiple data science personas participate in the daily operations of data logistics and intelligent business applications. Management and employees need to understand the big picture of data science to maximize collaboration efforts for these operations. This article will highlight the specialized roles and skillsets needed for the different data science tasks and the best tools to empower data-driven teams. You will come away from this article with a better understanding of how to support your own data science teams, and it is valuable for both managers and team members alike.
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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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ArangoSync: A Recipe for Reliability
Estimated reading time: 18 minutes
A detailed journey into deploying a DC2DC replicated environment
When we thought about all the things we wanted to share with our users there were obviously a lot of topics to choose from. Our Enterprise feature; ArangoSync was one of the topics that we have talked about frequently and we have also seen that our customers are keen to implement this in their environments. Mostly because of the secure requirements of having an ArangoDB cluster and all of its data located in multiple locations in case of a severe outage.
This blog post will help you set up and run an ArangoDB DC2DC environment and will guide you through all the necessary steps. By following the steps described you’ll be sure to end up with a production grade deployment of two ArangoDB clusters communicating with each other with datacenter to datacenter replication.
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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
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.
ArangoML Part 4: Detecting Covariate Shift in Datasets
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