adbdgl-adapter 2.1.0

Convert ArangoDB graphs to DGL & vice-versa.

ArangoDB-DGL Adapter

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

About DGL

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 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.

• Website
• Documentation
• Highlighted Features

Installation

Latest Release

pip install adbdgl-adapter

Current State

pip install git+https://github.com/arangoml/dgl-adapter.git

Quickstart

Also available as an ArangoDB Lunch & Learn session: Graph & Beyond Course #2.8

from arango import ArangoClient  # Python-Arango driver
from dgl.data 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(
    "fraud-detection",
    {"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)

Development & Testing

Prerequisite: arangorestore

1. git clone https://github.com/arangoml/dgl-adapter.git
2. cd dgl-adapter
3. (create virtual environment of choice)
4. pip install -e .[dev]
5. (create an ArangoDB instance with method of choice)
6. pytest --url <> --dbName <> --username <> --password <>

Note: A pytest parameter can be omitted if the endpoint is using its default value:

def pytest_addoption(parser):
    parser.addoption("--url", action="store", default="http://localhost:8529")
    parser.addoption("--dbName", action="store", default="_system")
    parser.addoption("--username", action="store", default="root")
    parser.addoption("--password", action="store", default="")