Sean Robinson

This workshop will cover a variety of graph data science techniques using Python, Neo4j, and other libraries. The goal of the workshop is to serve as a springboard for attendees to identify which graph-based tools/techniques can provide novel value to existing workflows.

In this session, we will look at how LLMs provide new options for capabilities within ETL pipelines. Specifically, we will look at how to use LLMs as a named entity recognition stage to extract entities from unstructured data and use them to enhance our graph representation of the data.

This workshop will cover a variety of graph data science techniques using Python, Neo4j, and other libraries. The goal of the workshop is to serve as a springboard for attendees to identify which graph-based tools/techniques can provide novel value to existing workflows.

The rise of graph analytics and network science within data has been steadily growing for several years. Today we see top companies offering new ways to leverage the interconnected power of networks to answer the increasingly complex and subtle questions businesses are asking of their data.