Double-AI in Bibliometric Analysis: Large Language Models meet Optimization at scale

Time: 10h00-11h00: Tuesday 18.08.2026

Location: Room 508, building A5, Institute of Mathematics (18 Hoang Quoc Viet, Nghia Do, Ha Noi).

Abstract: Academic performance indicators and university rankings rely heavily on bibliometric analysis. Recent advances in large language models (LLMs) provide powerful semantic representations of scientific publications, while large-scale citation databases such as Web of Science and OpenAlex enable the analysis of scholarly networks at unprecedented scale. Extracting reliable and interpretable knowledge from these data, however, requires principled mathematical models and scalable algorithms. In this talk, I will first present our vision of Double-AI, combining Artificial Intelligence with Algorithmic Intelligence, for bibliometric analysis at scale. Then, focusing on community detection in citation networks, I will discuss how optimization and variational analysis enable scalable algorithms, sparse and interpretable community memberships, and stable clustering solutions under network perturbations. The presentation is illustrated using real-world citation networks with up to billions of citation links. The talk is based on recent and ongoing research of the project Fully Algorithmic Librarian (https://fan.zib.de/).