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ArangoML

README.md

Graph ML is the future of data analytics

By 2025, graph technologies will be used in most of data and analytics innovations.

ArangoDB as the foundation for Graph ML

  • Scalable: Designed from ground up to scale Enterprise use cases
  • Simple Ingestion: Easy integration in existing data infrastructure + connectors to all leading data processing and data ecosystems
  • Open Source: Extensibility, Community, especially large community maintained library
  • NLP Support: Built-In Text Processing, Search, and Similarity Ranking
ArangoGraphML NetworkX, cuGraph, PyG & DGL Support
ArangoGraphML provides enterprise-ready graph-powered machine learning as a cloud service via Jupyter Notebooks that run on the ArangoGraph Insights Platform.

The Networkx Adapter, cuGraph Adapter, PyG Adapter, and DGL Adapter allow developers to export their ArangoDB graphs to NetworkX/cuGraph/PyG/DGL & vice-versa.

FastGraphML Pregel
FastGraphML is a low-code python package that enables the generation of graph embeddings with PyG.

ArangoDB has built-in Pregel support that allows for analytical processing directly on graphs stored in ArangoDB. Get started here.


Learn more from our resources at arangodb.com/graph-analytics-at-enterprise-scale

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  1. arangopipe arangopipe Public

    ArangoML Pipeline is a common and extensible Metadata Layer for Machine Learning Pipelines based on ArangoDB.

    Python 122 13

  2. fastgraphml fastgraphml Public

    Given an input graph (ArangoDB or PyG) it generates graph embeddings using Low-Code framework built on top of PyG.

    Python 68 8

  3. networkx-adapter networkx-adapter Public

    The NetworkX adapter for ArangoDB

    Python 24 8

  4. cugraph-adapter cugraph-adapter Public

    The RAPIDS cuGraph adapter for ArangoDB

    Python 3 1

  5. pyg-adapter pyg-adapter Public

    The PyTorch Geometric adapter for ArangoDB

    Python 5 2

  6. dgl-adapter dgl-adapter Public

    The Deep Graph Library adapter for ArangoDB

    Python 5 1

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