Graphs and Networks

Connections matter — in social media, biology, transportation, and beyond. This season maps out the study of graphs and networks, explaining core concepts like centrality and community detection, and exploring how real-world systems can be modeled and optimized through network analysis. With a balance of theory and application, each episode shows how understanding structure can unlock powerful insights.

Episodes

• Network of Past Guests Collaborations — Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.
• The Network Diversion Problem — Professor Pål Grønås Drange of the University of Bergen introduces **Parameterized Complexity**, a framework for tackling difficult computational problems by focusing on structural features that can m
• Complex Dynamics in Networks — Complex systems often make more sense once we map the relationships inside them. We learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of
• Github Network Analysis — Complex systems often make more sense once we map the relationships inside them. We'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manag
• Networks and Complexity — Complex systems often make more sense once we map the relationships inside them. Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we
• Actantial Networks — Connections can tell a story that individual observations cannot. Listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, instit
• Graphs for Causal AI — Utkarshani Jaimini of the University of South Carolina’s Artificial Intelligence Institute explores how AI can move beyond correlation to better understand **cause and effect**. By combining knowledge
• Power Networks — Benjamin Schäfer of the Karlsruhe Institute of Technology explores **Braess’s paradox**—the surprising phenomenon where adding a new connection to a network can actually make the system perform worse.
• Unveiling Graph Datasets — Bastian Rieck, a tenured professor of machine learning at the University of Fribourg and head of the AIDOS lab, brings a unique perspective combining pure mathematics, machine learning, and graph theo
• Network Manipulation — Graph thinking changes the focus from individual data points to how they interact. We talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, w
• The Small World Hypothesis — Kyle discusses the history and proof for the small world hypothesis.
• Thinking in Networks — Kyle asks Asaf questions about the new network science course he is now teaching. The conversation delves into topics such as contact tracing, tools for analyzing networks, example use cases, and the
• Fraud Networks — Complex systems often make more sense once we map the relationships inside them. We talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in Lon
• Criminal Networks — In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal netw
• Graph Bugs — Networks reveal patterns that disappear when data points are viewed alone. Today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work o
• Organizational Network Analysis — Complex systems often make more sense once we map the relationships inside them. Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organ
• Organizational Networks — Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and d
• Networks of the Mind — A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Data and Decision Sciences, uses to measure creativity in subj
• LLMs and Graphs Synergy — Powerful models become much more interesting when we look at what is happening underneath. Garima Agrawal, a senior researcher and AI consultant, brings her years of experience in data science and art
• A Network of Networks — Complex systems often make more sense once we map the relationships inside them. Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, exp
• Auditing LLMs and Twitter — Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their expertise on applying graph-based approaches to understanding both
• Fraud Detection with Graphs — Networks reveal patterns that disappear when data points are viewed alone. Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and grap
• Optimizing Supply Chains with GNN — Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how
• The Mystery Behind Large Graphs — Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable graph algorithms and compression techniques to tackle massi
• Customizing a Graph Solution — Connections can tell a story that individual observations cannot. Dave Bechberger, principal Graph Architect at AWS and author of "Graph Databases in Action", brings deep insights into the field of gr
• Graph Transformations — Sometimes the most important part of the data is the connection between the points. Adam Machowczyk, a PhD student at the University of Leicester, specializes in graph rewriting and its intersection w
• Networks for AB Testing — Sometimes the most important part of the data is the connection between the points. The data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok.
• Lessons from eGamer Networks — Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on understanding social and competitive success in multiplayer o
• Github Collaboration Network — Networks reveal patterns that disappear when data points are viewed alone. We discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a n
• Graphs and ML for Robotics — Sometimes the pattern is not in the data points—it is in the links between them. Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and plan
• Graphs for HPC and LLMs — The relationships between data points can matter as much as the data points themselves. Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Paralle
• Graph Databases and AI — Complex systems often make more sense once we map the relationships inside them. We sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving ro
• Network Analysis in Practice — Our new season "Graphs and Networks" begins here! We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf dis