ArangoDB v3.10 is under development and not released yet.

This documentation is not final and potentially incomplete.

DGL Adapter

The ArangoDB-DGL Adapter exports graphs from ArangoDB into Deep Graph Library (DGL), a Python package for graph neural networks, and vice-versa

The Deep Graph Library (DGL) is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning that, if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.


Watch this lunch & learn session to get an introduction and see how to use the DGL adapter.

The ArangoDB-DGL Adapter repository is available on Github. Check it out!


To install the latest release of the ArangoDB-DGL Adapter, run the following command:

pip install adbdgl-adapter


The following examples show how to get started with ArangoDB-DGL Adapter. Check also the interactive tutorial.

from arango import ArangoClient  # Python-Arango driver
from import KarateClubDataset # Sample graph from DGL

# Let's assume that the ArangoDB "fraud detection" dataset is imported to this endpoint
db = ArangoClient(hosts="http://localhost:8529").db("_system", username="root", password="")

adbdgl_adapter = ADBDGL_Adapter(db)

# Use Case 1.1: ArangoDB to DGL via Graph name
dgl_fraud_graph = adbdgl_adapter.arangodb_graph_to_dgl("fraud-detection")

# Use Case 1.2: ArangoDB to DGL via Collection names
dgl_fraud_graph_2 = adbdgl_adapter.arangodb_collections_to_dgl(
    {"account", "Class", "customer"},  # Vertex collections
    {"accountHolder", "Relationship", "transaction"},  # Edge collections

# Use Case 1.3: ArangoDB to DGL via Metagraph
metagraph = {
    "vertexCollections": {
        "account": {"Balance", "account_type", "customer_id", "rank"},
        "customer": {"Name", "rank"},
    "edgeCollections": {
        "transaction": {"transaction_amt", "sender_bank_id", "receiver_bank_id"},
        "accountHolder": {},
dgl_fraud_graph_3 = adbdgl_adapter.arangodb_to_dgl("fraud-detection", metagraph)

# Use Case 2: DGL to ArangoDB
dgl_karate_graph = KarateClubDataset()[0]
adb_karate_graph = adbdgl_adapter.dgl_to_arangodb("Karate", dgl_karate_graph)