In This Article Scientific Networks

  • Introduction
  • General Overviews
  • Classic Works
  • Journals

Sociology Scientific Networks
by
Elisa Bellotti
  • LAST MODIFIED: 28 November 2016
  • DOI: 10.1093/obo/9780199756384-0185

Introduction

Scientific networks represent the attempt to map the structure of science using network analysis. In scientific networks, nodes can represent various entities, like individual scientists, laboratories, academic institutions, scientific journals, published articles, and even words and topics in articles. Likewise ties can represent different forms of relations, like co-authorship, citations, co-occurrence of words, intellectual and material exchange between institutions, and the like. Depending on the network feature we can thus have collaboration networks (e.g., co-authorship, but also collaborations between institutions, laboratories, and the like), citation networks (relationships among scientific publications based on their citations), and semantic networks (analyzing the occurrence of specific words in a set of publications). The network analysis approach dates back to the study of patters of communications and citations in the search for what were initially called invisible colleges, and to the study of the complexities of overlapping co-authorships and patterns of citations in the tradition of bibliometric and scientometric studies. Within this tradition researchers have attempted to map and visualize scientific collaborations in various ways as well as clustering academic disciplines according to collaboration patters, in order to observe the macro organization of scientific disciplines and topics. Scientific networks have been studied by sociologists in the attempt to discover the social factors that play a role in the work of scientists; by historians and philosophers of sciences, who look at trends in scientific epistemologies and outcomes; by physicists, mathematicians, and computer scientists, who search for theories and techniques that can handle big data and complex mechanisms; by information scientists, for the optimization of system of classification and measurement of publications; and by economists, who are interested in scientific productivity and profitability. This makes the topic of scientific networks truly interdisciplinary, where different disciplinary contributions are integrated toward the common goal of understanding the organization of and evolution of science.

General Overviews

Few journals’ special issues and books have been published providing general overviews of the substantive and methodological areas of research in studying scientific networks. Shiffrin and Börner 2004 collects contributions that use various techniques to map knowledge domains, using the same data source. Introductive articles provide general coverage of methods, techniques, and practices in the field of scientific networks. They are followed by contributions that address methods to extract and organize information from large unstructured databases; to cluster articles, citations, or co-authors in order to identify communities of science; to track dynamic changes in the network structure and to understand the processes by which such networks evolve. Finally, various articles present methods to display and visually explore the results of large-scale database analyses. Scharnhorst, et al. 2012 collects essays that review and describe major threads in the mathematical modeling of science dynamics, covering stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. Batagelj, et al. 2014 uses citation networks, co-authorship networks, and more general data on the structure of national scientific systems as empirical examples for various techniques of visualization, pattern recognition, clustering, and simulation in network analysis.

  • Batagelj, V., P. Doreian, A. Ferligoj, and N. Kejzar, eds. 2014. Understanding large temporal networks and spatial networks: Exploration, pattern searching, visualization and network evolution. Hoboken, NJ: Wiley-Blackwell.

    E-mail Citation »

    This edited collection of contributions explores social mechanisms that drive network change and introduces the reader to models that detect patterns of changing structures in large temporal networks and spatial networks, using scientific networks as empirical examples.

  • Scharnhorst, A., K. Börner, and P. van den Besselaar, eds. 2012. Models of science dynamics. Encounters between complexity theory and information. Berlin and Heidelberg, Germany: Springer-Verlag.

    E-mail Citation »

    The edited book reviews and describes major threads in the mathematical modeling of science dynamics, with the intention of reaching a unifying framework in studying the structure and evolution of science. The book is introduced by chapters that define and operationalize the terminology used in the study of science, and review the history of mathematical approaches for the modeling of science.

  • Shiffrin, R. M., and K. Börner, eds. 2004. Mapping knowledge domains. Proceedings of the National Academy of Sciences 101. Suppl. 1.

    E-mail Citation »

    A collection of essays, published in a Proceedings of the National Academy of Sciences (PNAS) supplement, presented at the Arthur M. Sackler Colloquium on Mapping Knowledge Domains exploring and applying the techniques available to map knowledge domains using an electronic compilation of the full text documents from PNAS covering 7 January 1997 to 7 September 2002 (148 issues containing some 93,000 journal pages).

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