GraphGeeks Podcast

Hosted by Amy Hodler

Episodes

Language

EN-US

About the show

Graphs are the data model for connected data. Graph technology enables us to capture and compute over interdependent relationships. Join us to hear from experts and practitioners as we chat about the latest innovations and research. Visit GraphGeeks.org to learn more about our community.

Listen to episodes

41 recent

8 min

Graph Chat: Graphs & GenAI with Clair Sullivan

In this Graph Chat, Amy Hodler catches up with graph expert and consultant Clair Sullivan at the Open Data Science Conference to talk about Entity Resolution, GraphRAG, and catching fraud. Learn how concepts that used to be highly niche—like ontologies, semantics, and context graphs—are now entering mainstream tech workflows. And hear why we need to stop "vibe coding" everything and use knowledge graphs to deliver high-fidelity results.More Info on Clair and what she does: https://github.com/cj2001

6 min

Graph Chat: Graphs, LLMs, and Data Science with Dan Stevens (Adobe)

Listen to a quick hallway at the Open Data Science Conference (ODSC) with Amy Hodler and Daniell Stevens, Product Analytics at Adobe.In this impromptu conversation, Dan shares his takeaways from the conference, including a trick for using SQL to bridge the definition gap between data science and engineering teams. We also dive into his personal history with graph path languages, how graph technology is colliding with LLMs to solve messy data problems like entity resolution, and why getting out of your day-to-day silo at events like ODSC is so critical for data practitioners.

8 min

Graph Chat with Golven Leroy on Predicting Graph Costs & Moving Past "It Depends"

How do we move past the ultimate graph answer: "It depends"?Live from the Open Data Science Conference (ODSC), Amy Hodler sat down with Golven Leroy, graph researcher and data science lead at Graphable, to discuss groundbreaking core mathematics that could change how we calculate the compute cost, latency, and bounds of graphs and trees—independently of technology.We also dive into his top takeaways from ODSC, including tracking LLM experiments with MLflow, revolutionary chip/circuit design, and open-source frameworks like Agor for visualizing enterprise AI agents.Link to the referenced paper: https://msp.org/involve/2026/19-2/p05.xhtml

31 min

Geometry vs. Topology in AI: Roie Schwaber-Cohen (Pinecone) & Amy Hodler

Is the future of AI fluid and semantic, or rigid and structured? In this episode of Graph Geeks in Discussion, host Amy Hodler sits down with Roie Schwaber-Cohen, Head of DevRel at Pinecone. Together, they explore a fascinating philosophical and operational debate: the intersection of vector geometry (semantic distance) and graph topology (explicit connectivity). Tune in to discover why solving the next generation of AI challenges, from hallucinations to agentic long-term memory, requires fusing both worlds, and why we all might just need to "embrace the fuzziness."

31 min

Memgraph Zero & Federated GQL with Marko Budiselić

Is data copying dead? In this episode of the GraphGeeks Podcast, host Amy Hodler sits down with Marko Budiselić ("Buda"), Co-founder & CTO at Memgraph, to discuss their major new releases: Memgraph Zero and MemGQL.Discover how Memgraph is tackling the massive pain point of ETL pipelines by creating a federated GQL layer that queries data directly at the source—across Postgres, MySQL, Neo4j, ClickHouse, and more. Buda also shares how a centralized semantic layer is becoming essential for the future of AI agents.Key topics covered:The core architecture and vision behind Memgraph Zero.How MemGQL acts as a federated graph query engine.Moving beyond context graphs into environmental graphs for AI agents.The shift toward agentic data modeling and mapping.👉 Read more about the release: https://memgraph.com/docs/memgraph-zero 👉 Vote on the next data connectors in the Memgraph Community Roadmap Poll: forms.gle/2MLfWp24uwbJpsey8

8 min

Graph Chat with William Lyon on AI Agents, Graph Memory, and the Return to Neo4j

In this chat, Amy Hodler catches up with William Lyon to discuss his recent return to the Neo4j Product Team and his deep dive into the world of AI agent frameworks and memory.Wil discusses his hands-on workshop exploring a fascinating frontier: Graphs as the Memory for AI Agents. Moving beyond simple retrieval, Will explains how we can use graph technology to mirror human cognitive functions, including:Episodic Memory: Learning from user interactions.Semantic Memory: Building a canonical model of the world.Procedural Memory: Unlocking advanced, graph-based reasoning for agents.Whether you're interested in the latest in GraphRAG, the evolution of Knowledge Graphs in the age of LLMs, or just want to hear about Will’s journey through the startup ecosystem, this conversation is packed with insights that have only become more vital since they were recorded.

16 min

Graph Chat with Paco Nathan on AI as a Practice, Not a Product

Bryce Merkl Sasaki (Head of Marketing at gdotv) sits down with the "Gandalf of Graph Technology" himself, Paco Nathan (Senior DevRel at Senzing), at the Open Data Science Conference. Paco is a pioneer in neural networks and NLP since the 1980s, and a leading voice in the MLOps and Graph communities. In this chat, Paco cuts through the current hype bubble to discuss how graph technologies and entity resolution are solving high-stakes, real-world problems. From interdicting $3 trillion in dark money and human trafficking to fixing systemic data disconnects in government agencies through the NIEM semantic standard, this conversation explores the profound human impact of well-engineered data.Key Topics:Entity Resolution (ER): How connecting data points protects real people in the legal and financial systems.Semantic Standards: Using NIEM and SKOS for better context engineeringWhy the biggest bottleneck in AI isn't the math—it's designing UX & visualizations that humans can actually use.The AI Bubble: Paco’s perspective on the current industry jitters vs. the tangible tech that is ready for production.

7 min

Graph Chat with Michelle Yi on AI Evals, Causal Graphs and Community

What happens when two community-builders sit down at the Open Data Science Conference (ODSC)? They talk about the future of AI infrastructure and the importance of supporting the next generation of founders!In this episode, Amy Hodler of GraphGeeks is joined by Michelle Yi, co-founder of Generationship, to dive into: The "Eval" Crisis: Why 95% of GenAI POCs fail and how rigorous evaluation is the cure.Causality & Graphs: Moving beyond "ice cream and shark attacks" to understand the why behind AI predictions.Spatial Reasoning: How graphs are becoming essential for robotics and multimodal AI.Generationship: Michelle’s mission to fund and support early-stage female+ founders building the future of AI infrastructure.Connect with Michelle & Generationship:🔗 https://www.generationship.ai/

8 min

Graph Chat with Sony Green on the Evolution of Graph Intelligence

In this Graph Chat from ODSC, Sony Green (COO of Kineviz) joins Bryce Merkl Sasaki to discuss how graph technology is moving from a niche tool to a mainstream enterprise powerhouse. Highlights:The Spanner Graph Impact: Why Google’s entry into the graph space is a watershed moment for big data and high consistency.Human-Centric AI: A look at Knowledge Mapping—helping law enforcement and investigators find absolute truths in unstructured data without relying on AI-generated conclusions.No-Code Graphing: Introduction of the Graph Composer, a tool designed to map disparate data sources into a graph model with zero coding.Learn more about Kineviz: https://www.kineviz.com/

9 min

Graph Chat with Denise Gosnell on ROI, AI, and the Power of Connections

In this episode of Graph Geeks, recorded live at the Open Data Science Conference (ODSC) in San Francisco, Bryce Merkl-Sasaki sits down with graph pioneer Denise Gosnell, PhD. Drawing from her experience at DataStax and AWS Neptune, Denise shares why graph technology is the secret engine behind modern AI.Key HighlightsDenise argues that the current AI explosion isn't a separate trend but is actually building off previous innovations like graphs that now provide the essential context that AI systems require to function effectively.Successful graph-centric companies often see massive valuations because graph technology provides the shortest conceptual path from a business idea to technical implementation, allowing teams to ship faster and align more clearly.Denise discusses her new book, Tech Confidential: The Insider’s Playbook for Daring Entrepreneurs. It offers a roadmap for the tech life cycle, covering everything from managing professional egos and building collaborative teams to navigating successful company exits.https://www.techconfidential.ai/