Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will progress through the following concepts: path and structure finding, visualization, and graph storage on disk. We will also offer tutorial participants the option of one advanced topic overview, including the use of graphs alongside LLMs for knowledge retrieval, scalable alternatives to NetworkX including cuGraph, and the use of linear algebraic translation of graph problems to speed up computations.

In this tutorial, you will learn how to integrate Large Language Models (LLMs) directly into Python programs as thoughtfully-designed core components of the program rather than bolt-on additions. This hands-on session teaches design principles and practical techniques for incorporating LLM outputs into program control flow. We will use LlamaBot, an open-source Python interface to LLMs, focusing on local execution with local and efficient models.