2487. Nonconvex Optimization in Large-Scale Bibliometric Analysis
Invited abstract in session MC-37: Optimization algorithms and applications 1, stream Nonlinear Optimization.
Monday, 10:30-12:00Room: JUR – Seminar-Raum 43
Authors (first author is the speaker)
| 1. | Thi Huong Vu
|
| Digital Data and Information for Society, Science, and Culture, Zuse Institute Berlin |
Abstract
Clustering scientific publications in large-scale bibliometric datasets poses challenging nonconvex optimization problems. We first share practical experience with hard clustering via the Leiden algorithm, which recovers expert-aligned partitions but also reveals fragmented networks, reconnected using semantic similarity from large language models. We then address fuzzy clustering as a nonlinear optimization problem, introducing GPU-parallel algorithms scalable to billions of citations and techniques using first- and second-order information to improve solution quality and stability.
Keywords
- Optimization Models and Methods
- Big Data
Status: accepted
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