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Population ageing requires society to adjust by ensuring additional types of services and assistance for elderly people. These may be provided by either organized services and sources of informal social support. The latter are especially important since a lack of social support is associated with a lower level of psychological and physical well-being. During the Covid-19 pandemic, social support for the elderly has proven to be even more crucial, also due to physical distancing. Therefore, this study aims to identify and describe the various types of personal social support networks available to the elderly population during the pandemic. To this end, a survey of Slovenians older than 64 years was conducted from April 25 to May 4, 2020 on a probability web-panel-based sample (n = 605). The ego networks were clustered by a hierarchical clustering approach for symbolic data. Clustering was performed for different types of social support (socializing, instrumental support, emotional support) and different characteristics of the social support networks (i.e., type of relationship, number of contacts, geographical distance). The results show that most of the elderly population in Slovenia has a satisfactory social support network, while the share of those without any (accessible) source of social support is significant. The results are particularly valuable for sustainable care policy planning, crisis intervention planning as well as any future waves of the coronavirus.

The goal of the paper is to identify groups/clusters of countries with similar scientific collaboration “profiles” inside the group and to other groups of countries. The collaboration is described by co-authorship of a publication network that can be analyzed on the original co-authorship network. However, the network is dominated by large values in rows and columns of the most scientifically productive countries, which highly correlates with their sizes. This problem is especially relevant for countries with a big diversity such as post-Soviet countries. Normalization of the collaboration network allows making the countries’ collaboration comparable; therefore the question about its applicability and sensitivity to the collaboration structure is relevant. We analyze co-authorship networks of post-Soviet countries for the period 1993–2018. We use three types of network normalizations to make publication output of the countries comparable, namely affinity normalization, Jaccard normalization, and activity normalization. They provide different views on the scientific collaboration structure of the countries. We reveal the effect of the country size is the strongest when using the affinity normalization and it seems there is no countries ‘size effect for the activity normalization. Affinity normalizations reveal a big imbalance of collaboration between post-Soviet countries caused by their sizes. Russia has a great impact due to its size. Jaccard normalization reveals countries` collaboration is influenced by their neighborhood or by the size of national sciences. Activity normalization detects the research potential of a particular country. We also observe during the past twenty-five years the scientific collaboration has significantly changed, and the previously dominant position of Russia is decreasing. New groups of intense scientific collaboration have formed, affected by geographical neighborhood.

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.

Understanding the patterns and underlying mechanisms that come into play when employees exchange their knowledge is crucial for their work performance and professional development. Although much is known about the relationship between certain global network properties of knowledge-flow networks and work performance, less is known about the emergence of specific global network structures of knowledge flow. The paper therefore aims to identify a global network structure in blockmodel terms within an empirical knowledge-flow network and discuss whether the selected local network mechanisms are able to drive the network towards the chosen global network structure. Existing studies of knowledge-flow networks are relied on to determine the local network mechanisms. Agent-based modelling shows the selected local network mechanisms are able to drive the network towards the assumed hierarchical global structure.

This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks' blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account.

Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 yearsThis book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.Offers a clear and insightful look at the state of the art in network clustering and blockmodelingProvides an excellent mix of mathematical rigor and practical application in a comprehensive mannerPresents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arraysFeatures numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectivelyWritten by leading contributors in the field of spatial networks analysisAdvances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.

In the first part of the paper current initiatives and latest publications with several ideas and good practices for improving statistical literacy are highlighted. In the second part some recommendations for the main actors dealing with statistics are offered. These actors are: educational institutions, statistical offices and other statistical institutions, statistical societies and media. It is pointed out that the cooperation of these actors is essential for improving statistical literacy.

Social network data are prone to errors regardless their source. This paper focuses on missing data due to actor non-response in valued networks. If actors refuse to provide information, all values for outgoing ties are missing. Partially observed incoming ties to non-respondents and all other patterns for ties between members of the network can be used to impute missing outgoing ties. Many centrality measures are used to determine the most prominent actors inside the network. Using treatments for actor non-response enables us to estimate better the centrality scores of all actors regarding their popularity or prominence. Simulations using initial known blockmodel structures based on three most frequently occurring macro-network structures: cohesive subgroups, core-periphery models, and hierarchical structures were used to evaluate the relative merits of the treatments for non-response. The results indicate that the amount of non-respondents, the type of underlying macro-structure, and the employed treatment have an impact on centrality scores. Regardless of the underlying network structure, the median of the 3-nearest neighbors based on incoming ties performs the best. The adequacy (or not) of the other non-response treatments is contingent on the network macro-structure.

Coauthorship links actors at the micro-level of scientists. Through electronic databases we now have enough information to compare entire research disciplines over time. We compare the complete longitudinal coauthorship networks for four research disciplines (biotechnology, mathematics, physics and sociology) for 1986–2005. We examined complete bibliographies of all researchers registered at the national Slovene Research Agency. Known hypotheses were confirmed as were three new hypotheses. There were different coauthoring cultures. However, these cultures changed over time in Slovenia. The number of coauthored publications grew much faster than solo authored productions, especially after independence in 1991 and the integration of Slovenian science into broader EU systems. Trajectories of types of coauthorship differed across the disciplines. Using blockmodeling, we show how coauthorship structures change in all disciplines. The most frequent form was a core-periphery structure with multiple simple cores, a periphery and a semi-periphery. The next most frequent form had this structure but with bridging cores. Bridging cores consolidate the center of a discipline by giving it greater coherence. These consolidated structures appeared at different times in different disciplines, appearing earliest in physics and latest in biotechnology. In 2005, biotechnology had the most consolidated center followed by physics and sociology. All coauthorship networks expanded over time. By far, new recruits went into either the semi-periphery or the periphery in all fields. Two ‘lab’ fields, biotechnology and physics, have larger semi-peripheries than peripheries. The reverse holds for mathematics and sociology, two ‘office’ disciplines. Institutional affiliations and shared interests all impact the structure of collaboration in subtle ways.

Scientific collaboration (SC) has become a widespread feature of modern research work. While many social network studies address various aspects of SC, little attention has so far been given to the specific factors that motivate researchers to engage in SC at the individual level. In our article, we focus on the types and practices of SC that researchers in Slovenia engage in. We consider this topic by adopting a quantitative and qualitative methodological approach. The former was conducted through a web survey among active researchers, and the latter through in-depth interviews with a selected group of top researchers, i.e. intellectual leaders. Results show the extent of individual SC depends on the perceptions of researchers of the benefi ts of SC. Qualitative interviews additionally provide broader reflections on certain policy mechanisms that could better motivate Slovenian scientists to scientifi cally collaborate in the international arena.