Gene expression analysis to identify potential therapeutic targets
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Fraud and money laundering detection for financial institutions based on crypto or traditional transaction data
Route optimization for transport and logistics companies
Large-scale network planning for telecoms
SKU and distribution optimization for retailers with networks of warehouses and stores
Product recommendation for upsell and cross-sell for e-commerce vendors
Import data (from MB to TB) from a variety of sources by working directly with traditional data sources or graph databases
Use algorithms from a large library of graph operations, including Graph AI operations
Put together complex data processing pipelines where you can combine graph operations, classical data analysis operations, and machine learning
Experiment with different approaches by discovering graphs and interpreting algorithm results at any step of the calculations, and tuning parameters
Combine the benefits of a friendly "no code" GUI as well as coding via a powerful Python integration (code embedding, Python API, code generation)
Scale to billions of edges thanks to the underlying Apache Spark cluster computing engine
