@ruvector/graph-data-generator

You can generate synthetic graph data by configuring specific topologies, node and edge counts, and densities. This allows you to create test datasets to benchmark graph database performance under load without using sensitive real-world data.

Synthetic graph data can be exported into multiple formats including JSON, GraphML, CSV, and Cypher. This ensures compatibility with various graph databases and machine learning training pipelines.

Yes, you can use AI-powered property generation to create realistic node and edge properties. This strategy produces more lifelike datasets compared to random property assignment.

The synthetic graph generation supports configurable topology models such as scale-free and small-world. You can specify these models to match the structural characteristics needed for your machine learning training or social network simulation.

You can create a synthetic social network by specifying the desired number of user nodes and connection edges. The generator applies your chosen topology and AI-powered properties to produce a realistic network structure for testing.