TY - CHAP A1 - Yueksel Erguen, Inci A1 - Litzel, Ida A1 - Peng, Hanqiu T1 - Integrating Large Citation Datasets T2 - Operations Research Proceedings 2024. OR 2024 N2 - This paper explores methods for building a comprehensive citation graph using big data techniques to evaluate scientific impact more accurately. Traditional citation metrics have limitations, and this work investigates merging large citation datasets to create a more accurate picture. Challenges of big data, like inconsistent data formats and lack of unique identifiers, are addressed through deduplication efforts, resulting in a streamlined and reliable merged dataset with over 119 million records and 1.4 billion citations. We demonstrate that merging large citation datasets builds a more accurate citation graph facilitating a more robust evaluation of scientific impact. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-92575-7_7 SP - 46 EP - 52 ER - TY - GEN A1 - Vu, Thi Huong A1 - Litzel, Ida A1 - Koch, Thorsten T1 - Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives N2 - Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive databases like OpenAlex or Web of Science -- containing about 70 million articles and a billion citations -- poses significant challenges. We analyze potentials and challenges of the approach from both mathematical and computational perspectives. Among other things, second-order optimality conditions are established, providing new theoretical insights, and practical solution methods are proposed by exploiting the problem’s structure. Specifically, we accelerate the gradient projection method using GPU-based parallel computing to efficiently handle large-scale data. T3 - ZIB-Report - 25-09 KW - bibliometrics KW - fuzzy clustering KW - large-scale publication data KW - non-convex optimization KW - second-order optimality KW - gradient projection methods KW - Nesterov acceleration KW - GPU-based parallel computing Y1 - 2025 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10036 ER - TY - GEN A1 - Vu, Thi Huong A1 - Koch, Thorsten T1 - Clustering scientific publications: lessons learned through experiments with a real citation network N2 - Clustering scientific publications helps uncover research structures within bibliographic databases. Graph-based methods such as spectral, Louvain, and Leiden clustering are commonly used due to their ability to model citation networks. However, their effectiveness can diminish when applied to real-world data. This study evaluates these clustering algorithms on a citation graph of about 700,000 articles and 4.6 million citations from the Web of Science. The results show that while scalable methods like Louvain and Leiden perform efficiently, their default settings often yield poor partitioning. Meaningful outcomes require careful parameter tuning, especially for large networks with uneven structures, including a dense core and loosely connected papers. These findings highlight practical lessons about the challenges of large-scale data, method selection and tuning based on specific structures of bibliometric clustering tasks. T3 - ZIB-Report - 25-05 KW - graph clustering KW - citation networks KW - Web of Science KW - bibliometric analysis KW - unsupervised learning Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100418 ER - TY - JOUR A1 - Rong, Guoyang A1 - Chen, Ying A1 - Ma, Feicheng A1 - Koch, Thorsten T1 - Exploring Interdisciplinary Research Trends through Critical Years for Interdisciplinary Citation JF - Journal of Informetrics Y1 - 2025 U6 - https://doi.org/10.1016/j.joi.2025.101726 VL - 19 IS - 4 ER - TY - CHAP A1 - Stompor, Tomasz A1 - Zittel, Janina A1 - Koch, Thorsten A1 - Rusch, Beate T1 - Fully Algorithmic Librarian: Large-Scale Citation Experiments T2 - 20th International Society of Scientometrics and Informetrics Conference 2025, June 23-27, 2025 | Yerevan, Armenia N2 - The Fully Algorithmic Librarian (FAN) project explores application scenarios for algorithmic-intelligence(AI)-supported methods in academic libraries as central institutions for research support. To this end, the study builds on two algorithmic approaches for analyzing large-scale citation networks. A comparison of Web of Science (WoS) and OpenAlex structures using the PageRank algorithm reveals key differences. Additionally, a multi-label clustering technique designed for large-scale citation networks accounts for disciplinary variations in publication practices. Y1 - 2025 U6 - https://doi.org/10.51408/issi2025_204 ER - TY - JOUR A1 - Stompor, Tomasz A1 - Pampel, Heinz A1 - Boltze‐Fütterer, Julia A1 - Rusch, Beate T1 - DeepGreen—A Data Hub for the Distribution of Scholarly Articles From Publishers to Open Access Repositories in Germany JF - Learned Publishing N2 - DeepGreen is an automated delivery service for open access articles. Originally conceived to take advantage of the so-called open access component—a secondary publication right in Alliance and National licences in Germany to promote green open access—it aims to streamline open access processes by automating the distribution of full-text articles and metadata from publishers to repositories. The service, developed by a consortium and funded by the German Research Foundation (DFG) in its initial phase, has successfully established itself as a national service, facilitating open access content distribution and contributing to Germany's open access infrastructure. As of December 2024, DeepGreen distributes articles from 14 publishers to 84 institutional repositories and 6 subject-specific repositories. This article describes the role of the DeepGreen service in Germany, its collaboration with publishers and the potential of automated processes for storing articles in open access repositories, which, as publicly owned institutional infrastructures, ensure sustainable access and provide secure, redundant storage. KW - open access KW - open access repositories Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1002/leap.70000 SN - 0953-1513 VL - 38 IS - 2 PB - Wiley ER - TY - JOUR A1 - Vu, Thi Huong A1 - Xu, Hong-Kun A1 - Nguyen, Dong Yen T1 - Stability analysis of split equality and split feasibility problems JF - Journal of Global Optimization N2 - In this paper, for the first time in the literature, we study the stability of solutions of two classes of feasibility (i.e., split equality and split feasibility) problems by set-valued and variational analysis techniques. Our idea is to equivalently reformulate the feasibility problems as parametric generalized equations to which set-valued and variational analysis techniques apply. Sufficient conditions, as well as necessary conditions, for the Lipschitz-likeness of the involved solution maps are proved by exploiting special structures of the problems and by using an advanced result of B.S. Mordukhovich [J. Global Optim. 28, 347–362 (2004)]. These conditions stand on a solid interaction among all the input data by means of their dual counterparts, which are transposes of matrices and regular/limiting normal cones to sets. Several examples are presented to illustrate how the obtained results work in practice and also show that the assumption on the existence of a nonzero solution used in the necessity conditions cannot be lifted. Y1 - 2025 U6 - https://doi.org/10.1007/s10898-025-01469-6 SN - 0925-5001 VL - 92 SP - 411 EP - 429 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Rusch, Beate A1 - Peters-Kottig, Wolfgang A1 - Ceynowa, Klaus A1 - Dinter, Joachim A1 - Dubberke, Ina A1 - Happel, Hans-Gerd A1 - Hilliger, Kirsten A1 - Kitaeva, Xenia A1 - Kleineberg, Michael A1 - Koch, Thorsten A1 - Mc Leod, Shirley A1 - Mutter, Moritz A1 - Müller, Anja A1 - Seeliger, Frank A1 - Segger, Elisabeth A1 - Stanek, Ursula A1 - Wiese, Robert A1 - Wrzesinski, Marcel T1 - KOBV Jahresbericht 2023-2024 T3 - KOBV-Jahresbericht - 2023-2024 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100440 SN - 0934-5892 VL - 2023-2024 CY - Berlin ER - TY - JOUR A1 - Vu, Thi Huong A1 - Xu, Hong-Kun A1 - Nguyen, Dong Yen T1 - Stability of nonhomogeneous split equality and split feasibility problems with possibly nonconvex constraint sets JF - Optimization N2 - By applying some techniques of set-valued and variational analysis, we study solution stability of nonhomogeneous split equality problems and nonhomogeneous split feasibility problems, where the constraint sets need not be convex. Necessary and sufficient conditions for the Lipschitz-likeness of the solution maps of the problems are given and illustrated by concrete examples. The obtained results complement those given in [Huong VT, Xu HK, Yen ND. Stability analysis of split equality and split feasibility problems. arXiv:2410.16856.], where classical split equality problems and split feasibility problems have been considered. Y1 - 2025 U6 - https://doi.org/10.1080/02331934.2025.2508499 SP - 1 EP - 19 ER - TY - CHAP A1 - Kunt, Tim T1 - Solving the n-Queens Problem in Higher Dimensions T2 - Operations Research Proceedings 2024. OR 2024 N2 - How many mutually non-attacking queens can be placed on a d-dimensional chessboard of size n? The n-queens problem in higher dimensions is a generalization of the well-known n-queens problem. We present an integer programming formulation of the n-queens problem in higher dimensions and several strengthenings through additional valid inequalities. Compared to recent benchmarks, we achieve a speedup in computational time between 15–70x over all instances of the integer programs. Our computational results prove optimality of certificates for several large instances. Breaking additional, previously unsolved instances with the proposed methods is likely possible. On the primal side, we further discuss heuristic approaches to constructing solutions that turn out to be optimal when compared to the IP. KW - Integer Programming KW - Maximum Independent Set KW - n-Queens Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-92575-7_29 SP - 205 EP - 211 ER - TY - CHAP A1 - Kunt, Tim A1 - Buchholz, Annika A1 - Khebouri, Imene A1 - Koch, Thorsten A1 - Litzel, Ida A1 - Vu, Thi Huong T1 - Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings T2 - Operations Research Proceedings 2025. OR 2025 N2 - Large text data sets, such as publications, websites, and other text-based media, inherit two distinct types of features: (1) the text itself, its information conveyed through semantics, and (2) its relationship to other texts through links, references, or shared attributes. While the latter can be described as a graph structure and can be handled by a range of established algorithms for classification and prediction, the former has recently gained new potential through the use of LLM embedding models. Demonstrating these possibilities and their practicability, we investigate the Web of Science dataset, containing ~56 million scientific publications through the lens of our proposed embedding method, revealing a self-structured landscape of texts. T3 - ZIB-Report - 25-11 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100646 SN - 1438-0064 ER -