TigerGraph ML Workbench: Graph ML as a Service

TigerGraph’s Machine Learning Workbench is a Python-based toolkit that accelerates the development of graph-enhanced machine learning, which leverages the added insight from connected data and graph features for better predictions. The GitHub repository of the notebooks are found here.

Set Up Your Workbench

Step 1. Create DB credentials and set access permission

To access the graph database through pyTigerGraph, we need to create a database username and password, then put these credentials in config.json.

1. Go back to your browser tab/window for TigerGraph Cloud.
2. Click on Cluster on the left side menu. For the cluster containing this workbench, click Access Management.
3. Create a database user and grant appropriate permissions (e.g., globaldesigner). More details about managing database users can be found here: https://docs.tigergraph.com/cloud/security/manage-db-users. More details about access control settings can be found here: https://docs.tigergraph.com/tigergraph-server/current/user-access/access-control-model#_built_in_roles.

Step 2. Update the database credentials in config.json

1. After creating the login credentials in Step 1, go back to the ML Workbench and edit config.json in the root jupyter notebook folder to replace the host, username and password with your new credentials. Example: config.json

{
    "host": "https://subdomain.i.tgcloud.io",
    "username": "user_1",
    "password": "MyPassword1!",
    "getToken": true 
}

Note: For the host parameter, it is the domain name of the Cluster. You can find it in Cluster’s Details page which can be found by clicking on Clusters on tgCloud's left panel, then by clicking on the cluster’s name in the list (Details -> Network Information -> Domain). Replace the substring subdomain with your actual subdomain. Make sure to keep the “https://” at the beginning of the domain in the json config.

2. Once the credentials are updated, all the example notebooks and demos will refer to this config for database connections via pyTigerGraph. For example, here is how the algos/centrality.ipynb notebook connects to the database:

from pyTigerGraph import TigerGraphConnection
conn = TigerGraphConnection(
    host=config["host"],
    username=config["username"],
    password=config["password"]
)

Learn Graph ML from Example Notebooks

The ML Workbench comes with a collection of canonical Python notebooks that will introduce you to a number of features of the TigerGraph ML ecosystem.

• The basics directory contains notebooks on how to get started with pyTigerGraph.
• The algos directory contains notebooks for each category of algorithms within TigerGraph's Graph Data Science Library. You can run these algorithms via the pyTigerGraph Featurizer functionality.
• The GNNs directory contains tutorial notebooks on how to train GNNs using data stored in a TigerGraph database.
• The applications directory contains end to end demos of common applications such as fraud detection and recommendation.

We recommend starting with the tutorials in the basics folders if you are new to pyTigerGraph. Once you are familiar with our pyTigerGraph client, familiarize yourself with a few graph algorithms with the examples in the algos folder before going through the GNNs and end-to-end applications tutorials.

1. Getting Started with pyTigerGraph and GSQL

folder	notebook	intro
basics	datasets.ipynb	Load Data into TigerGraph
basics	feature_engineering.ipynb	Util functions about building graph features from TigerGraph
basics	pyTigergraph_101.ipynb	Basic pyTigerGraph examples
basics	gsql_101.ipynb	Basic GSQL 101 using pyTigerGraph
basics	gsql_102.ipynb	Advanced GSQL 102 (pattern match) using pyTigerGraph
basics	template_query.ipynb	How to call template query with pyTigerGraph

2. Graph Algorithms

folder	notebook	intro
algos	centrality.ipynb	Centrality algorithms
algos	community.ipynb	Community detection algorithms
algos	similarity.ipynb	Similarity algorithms
algos	pathfinding.ipynb	Pathfinding between vertices
algos	embedding.ipynb	Graph embedding algorithms
algos	classification.ipynb	Node classification algorithms
algos	topologicalLinkPrediction.ipynb	Topological link predictions

3. Graph Neural Networks with TigerGraph

folder	notebook	intro
GNNs/PyG	gcn_node_classification.ipynb	Node classification using PyG
GNNs/PyG	gcn_link_prediction.ipynb	Link prediction using PyG
GNNs/PyG	hgat_node_classification.ipynb	Heterogeneous Graph Attention Network using PyG
GNNs/DGL	gcn_node_classification.ipynb	Node classification using DGL
GNNs/DGL	rgcn_node_classification.ipynb	Heterogeneous Graph Convolutional Network using DGL
GNNs/Spektral	gcn_node_classification.ipynb	Node classification using Spektral for Tensorflow

4. End-to-end Applications using Graph ML

folder	notebook	intro
applications/fraud_detection	fraud_detection.ipynb	End-to-end fraud detection using Graph ML
applications/recommendation	recommendation.ipynb	End-to-end recommendation using Graph ML