ArangoDB v3.9 reached End of Life (EOL) and is no longer supported.

This documentation is outdated. Please see the most recent version at docs.arangodb.com

NetworkX Adapter

The ArangoDB-NetworkX Adapter allows you to export graphs from ArangoDB into NetworkX for graph analysis with Python, and vice-versa

NetworkX is a commonly used tool for analysis of network-data. If your analytics use cases require the use of all your graph data, for example, to summarize graph structure, or answer global path traversal queries, then using the ArangoDB Pregel API is recommended. If your analysis pertains to a subgraph, then you may be interested in getting the NetworkX representation of the subgraph for one of the following reasons:

An algorithm for your use case is available in NetworkX
A library that you want to use for your use case works with NetworkX Graphs as input

Resources

Check out this lunch & learn session to see how using this adapter gives you the best of both graph worlds - the speed and flexibility of ArangoDB combined with the ubiquity of NetworkX.

The ArangoDB-NetworkX Adapter repository is also available on Github.

Installation

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

pip install adbnx-adapter

Quickstart

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

from arango import ArangoClient # Python-Arango driver
from networkx import grid_2d_graph # Sample graph from NetworkX

from adbnx_adapter import ADBNX_Adapter

# 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="")

adbnx_adapter = ADBNX_Adapter(db)

# Use Case 1.1: ArangoDB to NetworkX via Graph name
nx_fraud_graph = adbnx_adapter.arangodb_graph_to_networkx("fraud-detection")

# Use Case 1.2: ArangoDB to NetworkX via Collection names
nx_fraud_graph_2 = adbnx_adapter.arangodb_collections_to_networkx(
    "fraud-detection", 
    {"account", "bank", "branch", "Class", "customer"}, # Vertex collections
    {"accountHolder", "Relationship", "transaction"} # Edge collections
)

# Use Case 1.3: ArangoDB to NetworkX 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": {},
    },
}
nx_fraud_graph_3 = adbnx_adapter.arangodb_to_networkx("fraud-detection", metagraph)

# Use Case 2: NetworkX to ArangoDB
nx_grid_graph = grid_2d_graph(5, 5)
adb_grid_edge_definitions = [
    {
        "edge_collection": "to",
        "from_vertex_collections": ["Grid_Node"],
        "to_vertex_collections": ["Grid_Node"],
    }
]
adb_grid_graph = adbnx_adapter.networkx_to_arangodb("Grid", nx_grid_graph, adb_grid_edge_definitions)

❮ Pregel Algorithms ArangoDB-DGL Adapter ❯