The paper introduces the k-means approach, one of the methods most often used for clustering »classical« data, to the blockmodeling of one-mode and linked networks, where linked networks include multilevel networks, dynamic networks, dynamic multilevel networks, and meta-networks. As this approach was significantly faster than previous approaches to blockmodeling linked networks, it significantly increased the size of the linked networks that could be analysed and thereby facilitated the first blockmodeling of a dynamic multilevel network.
COBISS.SI-ID: 36567901
The development and successful implementation of R&D policies depends on understanding patterns of scientific collaboration (SC). Existing studies on SC typically focus on the individual level, despite SC occurring on many interdependent social levels. Therefore, this paper provides a simultaneous insight into SC patterns among researchers (individual level) and among organizations (organizational level) in the social sciences. SC on the individual level is operationalized by co-authorship of a scientific paper whereas two organizations are said to collaborate if they share a research project. Based on data for the period 2006-2015 retrieved from Slovenian national information systems, two-level collaboration networks were formed with respect to researchers in the social sciences field. These networks were analyzed using a k-means-based blockmodeling approach for linked networks. The results show a high level of interdisciplinary SC and a large organizational impact on individual collaborations. On the individual level, a structure with several cohesive clusters and a semi-periphery appears while, on the organizational level, a kind a core-periphery structure emerges in which both the core and periphery can be split into several clusters. The most surprising result indicates that SC on the level of organizations is often not reflected in common published scientific papers on the individual level (and vice versa).
COBISS.SI-ID: 28497411
Researchers have extensively studied the social mechanisms that drive the formation of networks observed among preschool children. However, less attention has been given to global network structures in terms of blockmodels. A blockmodel is a network where the nodes are groups of equivalent units (according to links to others) from a studied network. It is already shown that mutuality, popularity, assortativity, and different types of transitivity mechanisms can lead the global network structure to the proposed asymmetric core-cohesive blockmodel. Yet, they did not provide any evidence that such a global network structure actually appears in any empirical data. In this paper, the symmetric version of the core-cohesive blockmodel type is proposed. This blockmodel type consists of three or more groups of units. The units from each group are internally well linked to each other while those from different groups are not linked to each other. This is true for all groups, except one in which the units have mutual links to all other units in the network. In this study, it is shown that the proposed blockmodel type appears in empirical interactional networks collected among preschool children. Monte Carlo simulations confirm that the most often studied social network mechanisms can lead the global network structure to the proposed symmetric blockmodel type. The units’ attributes are not considered in this study.
COBISS.SI-ID: 36567645
In this paper, we present the outer product decomposition of a product of compatible linked networks. It provides a foundation for the fractional approach in network analysis. We discuss the standard and Newman's normalization of networks. We propose some alternatives for fractional bibliographic coupling measures.
COBISS.SI-ID: 18940505
This chapter introduces blockmodeling of linked networks. Essentially, it is shown that the multilevel blockmodeling approach can also be used for other types of linked networks. The main goal of blockmodeling linked networks is to cluster nodes from all sets while taking all available information into account. By blockmodeling linked networks, the authors wish to blockmodel all one-mode and two-mode networks simultaneously so as to obtain one clustering for nodes from each set (although they occur in one one-mode network and at least one two-mode network). Examples of blockmodeling for two types of linked networks (a multilevel network and a network measured at several time points) are also presented.
COBISS.SI-ID: 36548957