Data Skeptic

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 (structured representations of information) are combined with neural networks in the field of neurosymbolic AI to represent and reason about complex relationships. This involves creating causal ontologies, incorporating the "weight" or strength of causal relationships and hyperrelations. This field has many practical applications such as for AI explainability, healthcare and autonomous driving. Follow our guest Utkarshani Jaimini's Webpage [https://utkarshani.github.io] Linkedin [https://www.linkedin.com/in/utkarshanijaimini/] Papers in focus CausalLP: Learning causal relations with weighted knowledge graph link prediction, 2024 [https://arxiv.org/abs/2405.02327] HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph, 2024 [https://arxiv.org/abs/2410.14679]

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 [https://x.com/manitapote] Google Scholar [https://scholar.google.com/citations?user=ukS-vPcAAAAJ&hl=en] Papers in focus Coordinated Reply Attacks in Influence Operations: Characterization and Detection ,2025 [https://arxiv.org/abs/2410.19272] Manipulating Twitter through Deletions,2022 [https://ojs.aaai.org/index.php/ICWSM/article/view/19355]

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

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.

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 network structure. Bavo will also present his iFraud simulator that can be used to model fraudulent networks for detection training purposes. Do you have a question about fraud detection? Bavo says he will gladly help. Feel free to contact him.   ------------------------------- Want to listen ad-free?  Try our Graphs Course?  Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com [https://plus.dataskeptic.com]