07-08, 08:00–12:00 (US/Pacific), Room 315
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, we will walk you through what we consider the most practical aspects of graph theory using NetworkX. While graph theory can seem abstract at first, having a computational framework like NetworkX makes it much more approachable.
We will start with what we think is the most intuitive way to understand graphs - seeing them as computational objects we can manipulate with code. From there, we will show you how we approach common tasks like finding paths between nodes, analyzing graph structure, and creating visualizations that actually make sense. We will also cover how to store and read graphs to/from disk.
Based on our experience working with graphs, we've selected three cutting-edge topics that we think are worth exploring: using graphs with LLMs for knowledge retrieval, scaling up to larger datasets with cuGraph and linear algebra, or an introduction to the use of graphs in deep learning. Tutorial participants will get to choose one of these topics live.
This tutorial is structured based on what we wished we knew when we first started working with graphs, and is structured in the order that we believe to be most productive for learning. By the end of the tutorial, participants should be able to productively prototype with graphs immediately!
Installation instructions will be available on the GitHub repository.
Prerequisites –Tutorial participants should be comfortable with writing for-loops and conditionals in Python. If they come with some graph theory background, even better, but not required. No knowledge of the NetworkX API is necessary, as it will be taught in class. Some NumPy and matplotlib knowledge will be useful.
As Senior Principal Data Scientist at Moderna Eric leads the Data Science and Artificial Intelligence (Research) team to accelerate science to the speed of thought. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of discovering medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.
Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He is also on the core developer team of NetworkX and PyMC. In addition, he gives back to the community through code contributions, blogging, teaching, and writing.
His personal life motto is found in the Gospel of Luke 12:48.