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Erschienen in:
2019 | OriginalPaper | Buchkapitel
8. Creation and Analysis of Large-Scale Bibliometric Networks
verfasst von : Kevin W. Boyack, Richard Klavans
Erschienen in: Springer Handbook of Science and Technology Indicators
Verlag: Springer International Publishing
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Abstract
In the more than a decade since the last Handbook of Quantitative Science and Technology Research [8.1] was published, a sea change has occurred in the creation and analysis of bibliometric networks that describe the Science & Technology (S&T) landscape. Previously, networks were typically restricted in size to hundreds or thousands of objects (papers, journals, authors, etc.) due to lack of data access and computing capacity. However, recent years have seen the increased availability of full databases, increased computing capacity, and development of partitioning and community detection algorithms that can work effectively at large scale. As a result, much larger networks–comprised of millions or tens of millions of objects–are being created and analyzed. These large-scale networks have enabled analyses that were simply not possible in the past, analyses that require the context of complete networks to give accurate results.
In this chapter, we focus on large-scale, global bibliometric networks, and on the types of analysis that are enabled by these networks. We start by providing a historical perspective that sets the stage for recent advances that have culminated in the ability to create and analyze large-scale bibliographic networks. We then discuss data sources and the methods that are commonly used to create large-scale networks. We review many of these networks, along with the types of unique analyses that they enable, and ways that their results can be effectively communicated. After reviewing the state of the art, we discuss our most recent large-scale topic-level model of science in detail as an example of a global bibliometric model and show how it can be used for various applications.