#GraphAlgorithms #GraphRAG #CausalGraphs Graphs, but not as you would expect them: Graph analytics guru Amy Hodler speaks to @JonKrohnLearns about the graph data structure and graph applications, graph algorithms, graph RAG, and graphs as memory systems for AI agents. We can use graphs in a surprising number of ways. Money laundering and fraud, as well as supply-chain crime, leave breadcrumbs at multiple “touch-points” over time, behaviors that graphs are better suited to reveal than rows and tables. Amy sees that most interest in graphs has been in the cybersecurity space. But this work isn’t only restricted to fighting crime! Listen to the episode to hear more case examples and how to get into graph work. This episode is brought to you by: • Dell: http://www.dell.com/dellproaistudio • Intel: https://www.dell.com/en-us/shop/dell-laptops/scr/laptops/appref=all-intel-processors-processor-brand • ODSC, the Open Data Science Conference: http://odsc.ai/ • Gurobi: http://gurobi.com/learn Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:01:49) A brief history of graphs • (00:10:08) Uncovering fraud with graphs • (00:30:05) Where graphs are most commonly applied, to date • (00:36:23) Retrieval augmented generation graphs • (00:50:08) The future of graphs Additional materials: https://www.superdatascience.com/923