Unveiling Graph Datasets

More from Data Skeptic All episodes →

1. 

   28 Jun 2025 · 56 min

   Complex Dynamic in Networks

   In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads. Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in network dynamics and complex systems ranging from biology to infrastructure and beyond. BarzelLab BarzelLab on Youtube Paper in focus: Universality in network dynamics, 2013

2. 

   22 Jun 2025 · 37 min

   Github Network Analysis

   In this episode we'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his engineering team by analyzing GitHub metadata - such as pull requests, issues, and discussions - as a bipartite graph of people and projects. Some insights we'll discuss are how network centrality measures (like eigenvector and betweenness centrality) reveal organizational dynamics, how vacation patterns influence team…

3. 

   14 Jun 2025 · 18 min

   Networks and Complexity

   In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are about the runtime of an algorithm based on its input size. For many graph problems, the interesting questions we want to ask take longer and longer to answer! This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.

4. 

   24 May 2025 · 41 min

   Graphs for Causal AI

   How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise. Knowledge graphs…

5. 

   16 May 2025 · 42 min

   Power Networks

6. 

   30 Apr 2025 · 41 min

   Network Manipulation

   In this episode we talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a focus on detecting coordinated manipulation campaigns on social media. Key insights include how coordinated reply attacks target influential figures like journalists and politicians, how machine learning models can detect these inauthentic campaigns using structural and behavioral features, and how deletion patterns reveal efforts to evade moderation or manipulate engagement metrics. Follow our guest X/Twitter Google Scholar Papers in focus Coordinated…

7. 

   21 Apr 2025 · 17 min

   The Small World Hypothesis

   Kyle discusses the history and proof for the small world hypothesis.

8. 

   12 Apr 2025 · 34 min

   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 importance of thinking in networks.

9. 

   1 Apr 2025 · 43 min

   Fraud Networks

   In this episode we talk with Bavo DC Campo, a data scientist and statistician, who shares his expertise on the intersection of actuarial science, fraud detection, and social network analytics. Together we will learn how to use graphs to fight against insurance fraud by uncovering hidden connections between fraudulent claims and bad actors. Key insights include how social network analytics can detect fraud rings by mapping relationships between policyholders, claims, and service providers, and how the BiRank algorithm, inspired by Google's PageRank, helps rank suspicious claims based on…

10. 

    17 Mar 2025 · 44 min

    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 networks. Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025. Listeners will learn how graph-based models assist law enforcement in analyzing missing data, identifying key figures in criminal organizations, and improving intervention strategies. Key insights include the…