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Register a new userComplex-network approach for visualizing and quantifying the evolution of a scientific topic
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Tracing the evolution of specific topics is a subject area which belongs to the general problem of mapping the structure of scientific knowledge. Often bibliometric data bases are used to study the history of scientific topic evolution from its appearance to its extinction or merger with other topics. In this chapter the authors present an analysis of the academic response to the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. Using a bibliographic database the distributions of Chornobyl-related papers in different scientific fields are analysed, as are their growth rates and properties of co-authorship networks. Elements of descriptive statistics and tools of complex-network theory are used to highlight interdisciplinary as well as international effects. In particular, tools of complex-network science enable information visualization complemented by further quantitative analysis. A further goal of the chapter is to provide a simple pedagogical introduction to the application of complex-network analysis for visual data representation and interdisciplinary communication.
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We analyze the reaction of academic communities to a particular urgent topic which abruptly arises as a scientific problem. To this end, we have chosen the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. The academic response is evaluated using scientific-publication data concerning the disaster using the Scopus database to present the picture on an international scale and the bibliographic database Ukrainika naukova to consider it on a national level. We measured distributions of papers in different scientific fields, their growth rates and properties of co-authorship networks. {The elements of descriptive statistics and the tools of the complex network theory are used to highlight the interdisciplinary as well as international effects.} Our analysis allows to compare contributions of the international community to Chornobyl-related research as well as integration of Ukraine in the international research on this subject. Furthermore, the content analysis of titles and abstracts of the publications allowed to detect the most important terms used for description of Chornobyl-related problems.
Novelty is an inherent part of innovations and discoveries. Such processes may be considered as an appearance of new ideas or as an emergence of atypical connections between the existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant concept (idea), whereas an edge between two nodes means that the corresponding concepts have been used in a common context. In this study we address the question about a possibility to identify the edges between existing concepts where the innovations may emerge. To this end, we use a well-documented scientific knowledge landscape of 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019. We extract relevant concepts for them using the ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding, we discuss the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear.
There is demand from science funders, industry, and the public that science should become more risk-taking, more out-of-the-box, and more interdisciplinary. Is it possible to tell how interdisciplinary and out-of-the-box scientific papers are, or which papers are mainstream? Here we use the bibliographic coupling network, derived from all physics papers that were published in the Physical Review journals in the past century, to try to identify them as mainstream, out-of-the-box, or interdisciplinary. We show that the network clusters into scientific fields. The position of individual papers with respect to these clusters allows us to estimate their degree of mainstreamness or interdisciplinary. We show that over the past decades the fraction of mainstream papers increases, the fraction of out-of-the-box decreases, and the fraction of interdisciplinary papers remains constant. Studying the rewards of papers, we find that in terms of absolute citations, both, mainstream and interdisciplinary papers are rewarded. In the long run, mainstream papers perform less than interdisciplinary ones in terms of citation rates. We conclude that to avoid a trend towards mainstreamness a new incentive scheme is necessary.
To quantify the mechanism of a complex network growth we focus on the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on copying/redirection/triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such verification is performed by measuring citation dynamics of Physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectory of similar papers, runaways or immortal papers with infinite citation lifetime etc. Thus, our most important finding is nonlinearity in complex network growth. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.
Modern scientific research has become largely a cooperative activity in the Internet age. We build a simulation model to understand the population-level creativity based on the heuristic ant colony algorithm. Each researcher has two heuristic parameters characterizing the goodness of his own judgments and his trust on literature. In a population with all kinds of researchers, we find that as the problem scale increases, the contributor distribution significantly shifts from the independent regime of relying on ones own judgments to the cooperative regime of more closely following the literature. The distribution also changes with the stage of the research problem and the computing power available. Our work provides some preliminary understanding and guidance for the dynamical process of cooperative scientific research in various disciplines.
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