科学概念网络:实证分析与建模

IF 0.7 4区数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Advances in Complex Systems Pub Date : 2021-08-09 DOI:10.1142/S0219525921400014

V. Palchykov, M. Krasnytska, O. Mryglod, Y. Holovatch

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引用次数: 3

摘要

某一科学领域内的概念是通过反映知识结构的内在联系联系起来的。为了获得对这种结构的定性洞察和定量描述，我们对物理领域的科学概念网络进行了实证分析和建模。为此，我们利用提交给电子出版物库arXiv的稿件集和ScienceWISE.info平台收集的科学概念词汇，构建了一个基于它们在出版物中共现的科学概念网络。由此产生的复杂网络具有许多特定的特征(高节点密度、无序性、结构相关性、倾斜的节点度分布)，这些特征不能被一些常用的网络模型所理解。我们表明，该模型基于同时考虑两个因素，即块增长和优先选择，给出了经验观察到的概念网络特性的解释。

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Network of scientific concepts: empirical analysis and modeling

Concepts in a certain domain of science are linked via intrinsic connections reflecting the structure of knowledge. To get a qualitative insight and a quantitative description of this structure, we perform empirical analysis and modeling of the network of scientific concepts in the domain of physics. To this end we use a collection of manuscripts submitted to the e-print repository arXiv and the vocabulary of scientific concepts collected via the ScienceWISE.info platform and construct a network of scientific concepts based on their co-occurrences in publications. The resulting complex network possesses a number of specific features (high node density, dissortativity, structural correlations, skewed node degree distribution) that can not be understood as a result of simple growth by several commonly used network models. We show that the model based on a simultaneous account of two factors, growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network.

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来源期刊

Advances in Complex Systems 综合性期刊-数学跨学科应用

CiteScore

1.40

自引率

0.00%

发文量

121

审稿时长

6-12 weeks

期刊介绍： Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.