Evolution of the computational science community: The dynamics of topics and collaborations in 24 years of ICCS and JoCS publications

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-05-10 DOI:10.1016/j.jocs.2025.102609

Lijing Luo , Klavdiya Bochenina , Tesfamariam M. Abuhay , Nachyn Dorzhu , George Kampis , Sergey Kovalchuk , Valeria Krzhizhanovskaya , Maciej Paszynski , Clélia de Mulatier , Jack Dongarra , Peter M.A. Sloot

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引用次数: 0

Abstract

We analyze the topic structure of 10,299 publications from the International Conference on Computational Science (ICCS) between 2001 and 2024 as well as the Journal of Computational Science (JoCS) between 2010 and 2023, using natural language processing techniques and network analysis. The computational science classification corpus was created into 15 main disciplines and 256 sub-disciplines sourced from Wikipedia. Among the 15 main disciplines, machine learning became the most popular topic after 2019, surpassing parallel & distributed computing, which peaked in the early 2010s. ICCS and JoCS show differences in research popularity in both first and second-level disciplines. Algorithm theory, Mathematical modeling, and network science are the most dominant topics in both ICCS and JoCS. Different disciplines present different trends in ICCS and JoCS. In the past 24 years, machine learning related topics have gained the most attention in both ICCS and JoCS. We also examined and compared the correlation between the trends in ICCS and Google search Trends. The collaboration of disciplinary networks of second-level disciplines exhibits a scale-free characteristic, and the network structures have undergone significant evolution over 24 years. Moreover, different disciplinary communities exhibit different ”introverted” and ”extroverted” community characteristics within the network. Additionally, we examined the life span of thematic workshops and the evolution of authors’ collaborations inside and after ICCS.

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计算科学社区的演变：24年ICCS和JoCS出版物中的主题和合作动态

我们使用自然语言处理技术和网络分析分析了2001年至2024年国际计算科学会议（ICCS）和2010年至2023年计算科学杂志（JoCS）的10,299篇出版物的主题结构。计算科学分类语料库从维基百科中创建了15个主要学科和256个子学科。在15个主要学科中，机器学习在2019年之后成为最受欢迎的话题，超过了并行&；分布式计算，在2010年代初达到顶峰。ICCS和JoCS在一、二级学科的研究受欢迎程度存在差异。算法理论、数学建模和网络科学是ICCS和JoCS中最主要的主题。不同学科的ICCS和JoCS呈现出不同的发展趋势。在过去的24年里，机器学习相关的话题在ICCS和JoCS中都得到了最多的关注。我们还检查并比较了ICCS趋势与谷歌搜索趋势之间的相关性。二级学科网络协作具有无标度特征，且网络结构在过去24年中发生了显著的演变。此外，不同的学科社区在网络中表现出不同的“内向”和“外向”社区特征。此外，我们还研究了专题研讨会的生命周期以及作者在ICCS内部和之后的合作演变。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).