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Research Collaboration Networks: What Do Researchers Say and What Networks Show?

Abstract This chapter focuses on the researcher, the knowledge worker. Relevant information emerges from two approaches to the study of research networks: (1) in the context of consolidated research groups from leading universities and (2) within the context of a new university, geographically isolated, but connected both nationally and globally, a new network.

Researchers from different disciplinary fields—Physics, Engineering, Social Sciences and Humanities, and Education—give their perceptions about networks issues, such as working in networks. We ask: what do researchers from two different countries say about networking? And, what does the analysis of their networks graphs show as an answer? The chapter ends by presenting a special case study, the biodiversity network.

Keywords Researcher’s perceptions • Physics • Engineering • Education • Biodiversity networks • Research networks graphs • Consolidated networks New networks

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Theoretical Approaches to Research Collaboration Networks
Abstract This chapter presents a pertinent literature review about theoretical approaches to research collaboration networks. Research networks are the object of study in the new science of networks. In this science domain, research network can be conceptualized as a web of connections among scientists...
(Evaluating Collaboration Networks in Higher Education Research: Drivers of Excellence)
What Do We Measure by Evaluating Research Collaboration Networks?
Abstract This chapter departs from the idea that collaborative processes must be diagnosed and monitored. Here is an introduction to evaluation tools, some of them very well known, such as rankings or bibliometric measures. We introduce the new metrics, the altmetrics. Here, we present answers...
(Evaluating Collaboration Networks in Higher Education Research: Drivers of Excellence)
Collaboration networks
A standard way to obtain the (first) collaboration network Co from the co-authorship network using network multiplication is we see that coy is equal to the number of works that authorsi and j wrote together. The weights in the first collaboration network are symmetric: In Co one...
(Understanding Large Temporal Networks and Spatial Networks)
Collaboration networks
In Section 3.5 we introduced matrix multiplication of two-mode networks and sketched the use of this in Section 4.8. Further, Section 3.6.2 outlined methods for constructing collaboration networks. Here we apply these methods to look at some collaboration networks providing different views of collaboration....
(Understanding Large Temporal Networks and Spatial Networks)
Macros for collaboration networks
The macro, normal, transforms a given two-mode network Jf into its normalized version n(M) using the following steps: Network/Create Vector/Centrality/Degree/Output Network/2-Mode Network/Partition into 2 Modes Operations/Vector+Partition/Extract Subvector [1] Vector/Transform/Invert Operations/Network+Vector/Vector#Network/Output...
(Understanding Large Temporal Networks and Spatial Networks)
An initial attempt of analyses of collaboration networks
The degree of an author in the first collaboration network is equal to the number of his/her different co-authors. In Table 4.11 the authors with the largest number of different co-authors for the centrality collaboration network are listed. Probably, many of these counted item represent more than one...
(Understanding Large Temporal Networks and Spatial Networks)
k-cores in collaboration networks
In a collaboration network a k-core is the largest subnetwork with the property that each of its author wrote a joint work with at least k other authors from the core. Figure 4.15 shows the cores of orders (values of k) 20-47. The bottom left k-core (marked in dark green)...
(Understanding Large Temporal Networks and Spatial Networks)
On the centrality and SNA collaboration networks
Based on the tools presented in Chapters 2 and 3, we have presented a series of analyses of the centrality literature and the collaboration network among social network analysts and contributors to network science. The results are readily interpretable and add to our understanding of these networks....
(Understanding Large Temporal Networks and Spatial Networks)
Adaptive Architecture to Support Context-Aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
Abstract. Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of...
(Virtual, Augmented and Mixed Reality)