Data Skeptic

In this discussion, Armin Pournaki, a Joint PhD candidate at the Max Planck Institute, unfolds the concept of Actantial Networks. He reveals how these graph-based structures can dissect political narratives, showcasing how conflicting stories arise around events like COVID-19 and the war in Ukraine. Pournaki also highlights how natural language processing helps visualize social media discourse, aiding in understanding polarization and narrative persuasion. His insights transform the way we perceive political communication in a divisive landscape.

Utkarshani Jaimini, a grad student at the University of South Carolina's Artificial Intelligence Institute, focuses on causal neurosymbolic AI. She explores how AI can distinguish cause from correlation using knowledge graphs. Jaimini discusses the practical implications for healthcare, including personalized models for conditions like pediatric asthma. Additionally, she addresses challenges in causal inference and the integration of weights in link prediction, all while emphasizing the importance of explainability in AI systems.

Benjamin Schaefer, an assistant professor at the Karlsruhe Institute of Technology, dives into the complex dynamics of energy systems. He discusses the Brass Paradox, illustrating how adding connections can lead to inefficiencies. Schaefer explores how AI can optimize energy production and consumption amidst change, addressing challenges like blackouts. He highlights the intricate balancing act in expanding energy networks and draws parallels to traffic systems, revealing how shortcuts can unexpectedly complicate efficiency.

Bastian Rieke, a tenured professor of machine learning at the University of Fribourg and leader of the Eidos Lab, dives deep into the world of graph datasets. He discusses the RINGS framework for evaluating dataset robustness and the significance of community dynamics in network analysis. Rieke highlights how topology can enhance machine learning performance by revealing data structure insights. He also addresses the ongoing challenges in graph learning and the necessity for better real-world datasets to foster innovation in research.

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 Reply Attacks in Influence Operations: Characterization and Detection ,2025 Manipulating Twitter through Deletions,2022

Explore the fascinating concept of the small world hypothesis, which reveals how interconnected social networks link people across great distances. Delve into empirical evidence, including Milgram's classic study and recent research on Facebook, illustrating our global interconnectedness. Discover how small world networks enable the rapid spread of ideas and even viruses, shedding light on their implications for algorithms and complexity. This riveting discussion highlights how understanding these connections can enhance efficiency across multiple domains.

Dive into the multidisciplinary world of network science, where teaching methods meet real-world applications. Discover how social interactions influence networks and the nuances of random versus clustered structures. Explore privacy concerns in the digital age, especially regarding blockchain technology. Uncover the pivotal role of contact tracing during the pandemic and its impact on public health. Finally, learn how effective storytelling can elevate data presentation and the importance of a 'thinking in networks' mindset.

Bavo DC Campo, a talented data scientist specializing in fraud detection and social network analytics, shares his insights on combating insurance fraud. He discusses how graph techniques reveal hidden links among fraudulent claims and actors. Bavo introduces the BiRank algorithm, akin to Google’s PageRank, which helps prioritize suspicious claims. His innovative iFraud simulator is also highlighted, showcasing its role in training models to detect fraud. The episode underscores the vital role of social networks in identifying patterns and trends in fraudulent activities.

In this discussion, Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute in London, delves into the intersection of network science and crime. He reveals how graph-based models can uncover key figures in criminal organizations and optimize law enforcement strategies. Key topics include the challenges of inaccurate data, innovative interventions to dismantle networks, and the role of machine learning in revealing hidden connections. Justin also highlights the need for improved data collection methods to better understand organized crime.

In this episode today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work on automated software testing for graph databases. Celine shows how fuzzing—the process of automatically generating complex queries—helps uncover hidden bugs in graph database management systems like Neo4j, FalconDB, and Apache AGE. Key insights include how state-aware query generation can detect critical issues like buffer overflows and crashes, the challenges of debugging complex database behaviors, and the importance of security-focused software testing. We'll also find out which Graph DB company offers swag for finding bugs in its software and get Celine's advice about which graph DB to use. ------------------------------- Want to listen ad-free?  Try our Graphs Course?  Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com