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.
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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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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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