Visualizing graph data and working with built-in ML benchmarks.
Visuals & Datasets
pyTigerGraph includes utilities to visualize your graph results directly in Jupyter notebooks and a collection of "stock" datasets to help you practice graph data science.
1. Graph Visualization
You can visualize schemas and query results using the ipycytoscape or networkx integrations.
pythonterminal# Visualize the schema of the connected graph conn.drawSchema() # Visualize a specific subgraph result results = conn.getVertices("Person", limit=50) conn.drawGraph(results)
2. Integrated Datasets
The datasets module allows you to ingest classic ML benchmark datasets into your TigerGraph instance with a single command. This is perfect for testing algorithms.
- Cora / Citeseer / Pubmed: Standard citation networks for node classification.
- FraudGraph: A synthetic dataset designed for fraud detection tutorials.
pythonterminalfrom pytigergraph.datasets import Datasets dataset = Datasets("Cora") dataset.ingest(conn)
3. Map Visualizations
If your vertices have geospatial attributes (latitude/longitude), you can export data to formats compatible with map visualization tools like Folium or Kepler.gl.
[!NOTE] Visualization functions are primarily designed for small to medium-sized subgraphs (up to a few thousand nodes). For visualizing billions of nodes, use TigerGraph Insights or GraphStudio.