Encounters between complexity theory and information. 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. Understanding large temporal networks and spatial networks: Exploration, pattern searching, visualization and network evolution. 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.īatagelj, V., P. 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. Finally, various articles present methods to display and visually explore the results of large-scale database analyses. 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. Introductive articles provide general coverage of methods, techniques, and practices in the field of scientific networks. Shiffrin and Börner 2004 collects contributions that use various techniques to map knowledge domains, using the same data source. 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.įew journals’ special issues and books have been published providing general overviews of the substantive and methodological areas of research in studying scientific networks. 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. 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. 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. 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). 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. 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. Scientific networks represent the attempt to map the structure of science using network analysis.