This research is within the frame of sociometric studies of science, particularly the application of social networks to co-authorship, and patterns of citations among researchers in Psychiatry and Neurosciences, General Psychology, and Experimental Psychology. This chapter applies Social Network Analysis to information retrieval from a multidisciplinary database; subject headings lists are not considered sufficient or sufficiently flexible to describe relationships between the sciences. The aim is also to identify similarities and differences among these areas according to bibliometric and network indicators. Social Network Analysis used to select scientific articles within a discipline overcomes the rigidity of information retrieval based on a preselected set of topics. Network graphs can be used to show working groups that otherwise would remain hidden. It is useful, also, to overlap networks of co-authorship (explicit relations) and patterns of cited references (implicit relations), which allow comparison between individual author or groups and the whole group. Finally, the author highlights the need to adapt assessment indicators from different scientific areas to allow consideration of the characteristics of diverse disciplines, based not only on the productivity of individual authors, but also their capacity to mediate with other actors and works within the research system.
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SNA has made a major contribution to the level of analysis of scientific research; it allows meso level examination (groups of researchers as the unit of analysis) even when working with large volumes of data, such as national scientific production (He, Ding & Ni, 2011).
The most widespread applications of bibliometrics to SNA are mapping of science, that is, thematic networks typically built from citations (Boyack, Klavans & Borner, 2005; Iñiguez, Muñoz Justicia, Peñaranda & Martínez, 2006; Leydesdorff, 2004; Leydesdorff & Rafols, 2009; Moya-Anegón, Vargas-Quesada, Herrero-Solana, Chinchilla-Rodriguez, Corera-Alvarez, Munoz-Fernandez, 2004), and studies of scientific collaboration, based on personal networks built from co-authorship of papers (Acedo, Barroso, Casanueva, Galan, 2006; Bozeman & Corley, 2004; Olmeda-Gómez, et al, 2009a; Perianes-Rodríguez, Olmeda-Gomez, Moya-Anegon, 2010). Personal networks based on copresence on dissertation committees (Martín, del Olmo Martínez, Gutiérrez, 2006; Casanueva Roche, Escobar Pérez, Larrinaga González, 2007; Delgado López-Cózar, Torres-Salinas, Jiménez-Contreras & Ruiz-Pérez, 2006; Olmeda-Gómez, et al, 2009b), or competitive examinations panels (Sierra, 2003) are also examined. However, its application to many other information sources are still unexplored.
A visual representation of a network provides the opportunity to analyze its structural properties: “By mapping the structure of interactions, a researcher can identify the channels through which information flows from one node to another and the potential for a corresponding influence of one over another” (Schultz-Jones, 2009, p. 595).