Quantitative Foundation Distinguished Professor
School of Data Science
University of Virginia

CV: cv.pdf
Email: yyahn@virginia.edu
Office: 1919 Ivy Rd., Room 444

Hello! 👋 Welcome to my homepage! You can find news, YY’s bio, and selected publications below. See Y Lab for more information about my research group & open positions, and Research for the research overview and the full list of publications. Use this form to indicate your interest joining my group!

I go by “YY” and you can find me on various social platforms below.

News

• 2025-12-09 YY will be a plenary speaker at 20th anniversary addition of NetSci conference in Boston! See my LinkedIn post.
• 2025-11-14 Congrats Byunghwee Lee for winning the “Best in Show” award at SDS Datapalooza 2025! Check out the coverage from SDS News!
• 2025-11-13 YY gave a keynote speech at CIKM‘25. See the News at SDS
• 2025-10-29 YY is giving a talk at the Information Sciences and Technology Department at George Mason University.
• 2025-10-15 YY will give a talk at Stanford University Graduate School of Business Organizational Behavior Seminars.
• 2025-09-16 YY will give a talk at the K-DS Distinguished Lecture Series in Korea on 11/21 KST.
• 2025-09-16 YY will serve as an Associate Editor for the AAAS journal Science Advances.
• 2025-09-06 Paper published! Our review paper “One pathogen does not an epidemic make: a review of interacting contagions, diseases, beliefs, and stories” has been published in npj complexity! See Hebert Dufresne2025one.
• 2025-08-31 Paper accepted! Devin’s paper “Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition‑Informed Approach to Quantifying Identity Fusion from Text” has been accepted in EMNLP‘25! See Wright2025cognitive.

Bio

Yong-Yeol (YY) Ahn is a network and data scientist whose work combines network science, machine learning, and the study of complex social, biological, and information systems. He is a Quantitative Foundation Distinguished Professor at the University of Virginia’s School of Data Science. Before joining UVA, he was a Professor at Indiana University’s CNetS, Luddy School of Informatics, Computing, and Engineering and a Visiting Professor at MIT. Earlier, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after completing his PhD in Statistical Physics from KAIST. His research focuses on the architectures of complex systems—how networks shape behavior, cognition, and scientific progress—and on developing methods in network analysis, machine learning, and natural language processing to investigate these mechanisms at scale. He is the co-author of Working with Network Data. His work has been recognized with several honors, including the Microsoft Research Faculty Fellowship.

Research

We study the hidden architectures of complex systems through network science and machine learning. Inspired by real-world problems, we develop network science, machine learning, and natural language processing methods; we leverage deep understanding of these methods to find novel solutions for real-world challenges. Some highlights below & See Research for the full publication list:

Interpretable embedding space and its applications

• Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space (under review in PNAS).
• A semantic embedding space based on large language models for modelling human beliefs (Nature Human Behaviour, 2025).
• Uncovering simultaneous breakthroughs with a robust measure of disruptiveness (under review in Science Advances).
• Unsupervised embedding of trajectories captures the latent structure of scientific migration (PNAS, 2023).
• Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations (Science Advances, 2021).

Network science and machine learning

• Implicit degree bias in the link prediction task (ICML‘25)
• Network community detection via neural embeddings (Nature Communications, 2024).
• Residual2Vec: Debiasing graph embedding with random graphs (NeurIPS‘21).
• Link communities reveal multiscale complexity in networks (Nature, 2010).

Science of Science

• Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents (under review in Nature Communications).
• Persistent Hierarchy in Contemporary International Collaboration (submitted).
• Cooperation and interdependence in global science funding (submitted).
• Uncovering simultaneous breakthroughs with a robust measure of disruptiveness (under review in Science Advances).
• Unsupervised embedding of trajectories captures the latent structure of scientific migration (PNAS, 2023).
• Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations (Science Advances, 2021).
• The latent structure of global scientific development (Nature Human Behaviour, 2022).
• Factors affecting sex-related reporting in medical research: a cross-disciplinary bibliometric analysis (Lancet, 2019).

Future of work and AI

• The Potential Impact of Disruptive AI Innovations on U.S. Occupations (submitted).
• AI exposure predicts unemployment risk: A new approach to technology-driven job loss (PNAS Nexus, 2025).

Beliefs, contagion, culture

• A semantic embedding space based on large language models for modelling human beliefs (Nature Human Behaviour, 2025).
• Sameness entices, but novelty enchants in fanfiction online (Humanities & Social Sciences Communications, 2025).
• Emergence of simple and complex contagion dynamics from weighted belief networks (Science Advances, 2024).
• The effectiveness of backward contact tracing in networks (Nature Physics, 2021).
• A Network Framework of Cultural History (Science, 2014).
• Flavor network and the principles of food pairing (Scientific Reports, 2011).

Biology and neuroscience

• Cooperative and Competitive Spreading Dynamics on the Human Connectome (Neuron, 2015).
• Evidence for Network Evolution in an Arabidopsis Interactome Map (Science, 2011).