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DGL Adaptor

Introducing the ArangoDB-DGL Adapter

Estimated reading time: 15 minutes

We are proud to announce the GA 1.0 release of the ArangoDB-DGL Adapter!

The ArangoDB-DGL Adapter exports Graphs from ArangoDB, a multi-model Graph Database, into Deep Graph Library (DGL), a python package for graph neural networks, and vice-versa.

On December 30th, 2021, we introduced to the ArangoML community our first release of the DGL Adapter for ArangoDB. We worked closely with our existing ArangoDB-NetworkX Adapter implementation to aim for a consistent UX across our (growing) Adapter Family. You can expect the same developer-friendly options, along with a helpful getting-started guide via Google Colab. And as always, it is open source!

This blog post will serve as a walkthrough of the ArangoDB-DGL Adapter, via its official Jupyter Notebook.

We will cover the following use cases:

  1. ArangoDB to DGL
    1. Via an ArangoDB graph
    2. Via a set of ArangoDB collections
    3. Via a user-defined metagraph
    4. Unique cases in attribute transfer
  2. DGL to ArangoDB
    1. Homogeneous graphs
    2. Heterogeneous graphs
    3. Unique cases in attribute transfer
[nbconvert url=”https://github.com/arangoml/dgl-adapter/blob/master/examples/outputs/ArangoDB_DGL_Adapter_output.ipynb”]

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Anthony rsz img

Anthony Mahanna

Anthony is an Honours Computer Science student at the University of Ottawa, Canada. He first discovered ArangoDB’s multi-model services while working on his image repository side project. After presenting his side project in an ArangoDB Community Pioneer session, Anthony transitioned to working with the Core & ML teams as an SWE intern.

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