Data Skeptic

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.

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them.  The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute for Mathematics in the Sciences and the Laboratoire Lattice (ENS-PSL), who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science.  Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the war in Ukraine. Listeners will also discover how this approach helps make large-scale discourse—from millions of tweets or political speeches—more transparent and interpretable, offering tools for studying polarization, issue alignment, and narrative-driven persuasion in digital societies. Follow our guest Armin Pournaki's Webpage [https://pournaki.com] Twitter/X [https://x.com/apournaki] Bluesky [https://bsky.app/profile/pournaki.bsky.social] Papers in focus How influencers and multipliers drive polarization and issue alignment on Twitter/X, 2025 [https://arxiv.org/abs/2505.14280v1] A graph-based approach to extracting narrative signals from public discourse, 2024 [https://arxiv.org/abs/2411.00702]

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.