11 Getting Started with the Graph Visualization Extension in Jupyter Environments

The oraclegraph widget enables interactive graph visualization directly in Jupyter Notebooks.

It supports loading graph data from JSON files, Oracle AI Databases (or earlier Oracle Database versions) using graph queries. You can customize the visualizations with feature flags, settings, and rule-based styling.

Before you begin, note that the graph visualization extension is supported in:

• JupyterLab 4 or higher
• Jupyter Notebook 7.x

Note:

You can find pre-built example notebooks demonstrating the oraclegraph features in the jupyter-notebooks/examples folder on GitHub.

The following sections describe the graph visualization widget in greater detail.

• Installing the Graph Visualization Extension for Jupyter
  Learn to install the graph visualization extension in your Jupyter environment.
• Basic Example with GraphVisualization
  Learn to set up and display a graph visualization in your Jupyter notebook using a graph configuration from a JSON file.
• Custom Styling Example with GraphVisualization
  The example in this section describes how to load graph data from a JSON file, configure feature flags, base styles, and rule-based styles, and finally display the graph using GraphVisualization.
• Example of Applying Styles Programmatically with GraphVisualization
  The example in this section describes how to programmatically set up feature flags, rule-based styles, base styles, and other settings options using GraphVisualization.
• Example Using SqlGraphVisualization and PgxGraphVisualization
  You can visualize SQL property graph queries using the oraclegraph Jupyter widget for interactive graph visualization. Additionally, you can load the graph into the graph server (PGX), run PGQL queries, and visualize the graph.
• oraclegraph API Reference
  This section provides the Python API reference information on the different classes supported in the oraclegraph package.