This paper examines the collaboration dynamics of institutions in the context of project collaboration in European Framework programmes. Its goal is to identify the key factors that promote the formation of collaborative ties among institutions as well as factors that influence the maintenance of collaborative ties when they are already formed. The paper broadly presents the theoretical starting points for the study of scientific networks within the EU, and it is in its empirical part organised around five hypotheses shaped on characteristics of analysed institutions and their positions within the dynamic network. Using valued blockmodeling, the study analyzes data from EU FP projects in the "Food, Agriculture, and Fisheries" theme between 2008 and 2014. The analysis reveals a complex hierarchical structure, characterized by a core–semi-periphery–periphery global structure. The findings confirm the significant role of coordinating institutions in driving European scientific collaboration and highlight the persistent challenges faced by organizations from new member states in securing influential positions. The study also identifies a distinct pattern of lasting collaboration dominated by established European research institutions, with non-European organizations playing limited role in the network.

Scientific collaboration among universities is traditionally studied by counting coauthored papers with different types of partners. Under this approach, the internal mechanisms of collaboration remain hidden and, without additional normalization, the results can be biased. In our study, we apply co-authorship network analysis to examine collaboration structures within university research networks. We use OpenAlex data from 2017 to 2019 for thirty universities of different ages and status. Our approach constructs normalized co-authorship networks by applying two bipartite normalization approaches (Standard and Strict), and then examines two Ps-core variants: (i) a fixed-size core comprising approximately 100 of the most productive and collaborative authors, and (ii) a fixed-strength core consisting of authors whose fractional contribution is for at least one paper. Our results show that the analyzed universities exhibit high levels of intra-organizational collaboration but markedly different collaboration structures. These differences are reflected in authors’ connectedness, the strength of collaboration, and the balance between external and internal affiliations. Established universities display more diverse collaboration structures than younger and catching-up universities. We discuss the potential benefits and risks associated with differing core compositions and propose policy recommendations based on our findings.

The study explores a dynamic co-authorship network among European countries between 1990 and 2023 that arose from an interplay of geographical, linguistic, and historical factors which influenced the scientific collaboration. In the analysis, bibliometric data from OpenAlex are used in Indirect Blockmodeling (IB) and Dynamic Stochastic Blockmodeling (DSBM) to examine patterns in three critical periods: rise of the Internet (1994–2003), enlargement of the EU (2004–2013), and the European Research Area (ERA) initiatives (2014–2023). The findings reveal exponential growth in the number of co-authored publications, an overall increase in intra-cluster collaboration, notably in the Balkan, Scandinavian, and Western clusters, coupled with persistent regional disparities. Despite EU policy interventions, collaborations between Western and non-Western regions remain limited. The study shows the need for targeted measures to ensure scientific networks across Europe are more inclusive and balanced.

Rand (1971) proposed what has since become a well-known index for comparing two partitions obtained on the same set of units. The index takes a value on the interval between 0 and 1, where a higher value indicates more similar partitions. Sometimes, e.g. when the units are observed in two time periods, the splitting and merging of clusters should be considered differently, according to the operationalization of the stability of clusters. The Rand Index is symmetric in the sense that both the splitting and merging of clusters lower the value of the index. In such a non-symmetric case, one of the Wallace indexes (Wallace, 1983) can be used. Further, there are several cases when one wants to compare two partitions obtained on different sets of units, where the intersection of these sets of units is a non-empty set of units. In this instance, the new units and units which leave the clusters from the first partition can be considered as a factor lowering the value of the index. Therefore, a modified Rand index is presented. Because the splitting and merging of clusters have to be considered differently in some situations, an asymmetric modified Wallace Index is also proposed. For all presented indices, the correction for chance is described, which allows different values of a selected index to be compared.

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.

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.

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.

Slovenija sodi med hitro starajoče se države, kar zahteva prilagoditve družbe na različnih področjih. Eno izmed njih je skrb za starejše, ki zaradi težav, povezanih s staranjem, potrebujejo vrsto storitev in pomoči. Del potrebne pomoči lahko pokrijejo formalne storitve (kot je osebna pomoč na domu), zelo pomembni pa so tudi neformalni viri socialne opore (sorodniki, prijatelji, sosedi). Raziskave kažejo, da ima del starejših razmeroma zadostno socialno oporo, obstaja pa tudi nezanemarljiv delež starejših s šibko socialno oporo ali brez virov neformalne socialne opore. Ti so lahko še posebej ranljivi v okoliščinah, kot je pandemija koronavirusa SARS-CoV-2. Tako članek naslavlja značilnosti omrežij socialne opore starejših, ki živijo doma, v času popolnega zaprtja javnega življenja v prvem valu pandemije, in sicer na podlagi podatkov, zbranih v okviru spletnega panela JazVem. Gre za verjetnostni vzorec 605 oseb, starejših od 64 let. V raziskavi so bile merjene čustvena in instrumentalna opora, opora v smislu neformalnega druženja, pa tudi elementi formalne opore in različne značilnosti omrežij. V vzorcu je bila dobra desetina starostnikov z omejenimi viri socialne opore. To so starostniki, ki niso navedli nobenega vira socialne opore, in starostniki s samo oddaljenimi viri socialne opore. Na število in dostopnost virov socialne opore v času pandemije vplivata spol in velikost gospodinjstva starostnika.

Changes in patterns of collaboration between Russian universities after the commencement of the Russian university excellence initiative (Project 5-100) are studied in this paper. While this project aimed to make leading Russian universities more globally competitive and improve their research productivity, it also happened to increase their cooperation. An analysis of affiliations and the co-authorship networks was conducted to explore scientific collaborations between and within the participating universities. Such analysis facilitates the investigation of the number of collaborations with other organizations, both domestic and international cooperation, and disciplinary differences. By analyzing the co-authorship networks, the position of universities in the academic network and the structure of collaborations among the participants were examined. A sample of 30 Russian universities, including participants in Project 5-100 and a control group of institutions with similar characteristics, was used. After joining the project, the participating universities increased both their cooperation with each other as well as with foreign universities and research institutions of the Russian Academy of Sciences, especially in the high-quality segment. At the same time, the collaboration patterns of non-participating universities did not change significantly. The centrality of Project 5-100 universities in the global academic network has increased, along with their visibility and coupling in the national network. The historical division between university and academic sectors has diminished, while the participating universities have started to play a more important role in knowledge production within the country.

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.

The paper addresses the relationship between different local network mechanisms and different global network structures, described by blockmodels. The research question is narrowed to the context of preschool children networks. Based on the studies regarding friendship, liking and interactional networks among preschool children, the popularity, transitivity, mutuality and assortativity mechanisms are assumed to be important for the evolution of such networks. It is assumed that the global network structure is defined by an asymmetric core-cohesive blockmodel consisting of one core group of units and two or more cohesive groups of units. Therefore, the main research question is whether the emergence of an asymmetric core-cohesive blockmodel can be a result of the influence of the listed mechanisms. Different initial global network structures are considered. Monte Carlo simulations were used. The relative fit measure is proposed and used to compare different blockmodel types on generated networks. The results show that the listed mechanisms indeed lead to the assumed global network structure.

This conclusion presents some closing thoughts on the concepts covered in the preceding chapters of this book and makes additional suggestions regarding potential future work. The book presents a wide variety of approaches and methods related to network clustering. It suggests that the institutional structure of science has a very large impact on the generation of scientific knowledge and the generation of scientific citation networks. The book advocates the optimization approach to the clustering problem. Using an appropriate criterion function, people can express their clustering goals, including the reduction of complexity, understanding network structures, and modeling networks. Together, optimization and the sought goals help define the nature of a “good” clustering. The book finishes by stressing two very general ideas. One is the importance of the exchange of ideas between different approaches with the goal of strengthening them. The second is the coupling of network processes and network structures to help readers understand both.

In 2018, the European Strategic Forum for research infrastructures (ESFRI) was tasked by the Competitiveness Council, a configuration of the Council of the EU, to develop a common approach for monitoring of Research Infrastructures' performance. To this end, ESFRI established a working group, which has proposed 21 Key Performance Indicators (KPIs) to monitor the progress of the Research Infrastructures (RIs) addressed towards their objectives. The RIs were then asked to assess their relevance for their institution. The paper aims to identify the relevance of certain indicators for particular groups of RIs by using cluster and discriminant analysis. This could contribute to development of a monitoring system, tailored to particular RIs. To obtain a typology of the RIs, we first performed cluster analysis of the RIs according to their properties, which revealed clusters of RIs with similar characteristics, based on to the domain of operation, such as food, environment or engineering. Then, discriminant analysis was used to study how the relevance of the KPIs differs among the obtained clusters. This analysis revealed that the percentage of RIs correctly classified into five clusters, using the KPIs, is 80%. Such a high percentage indicates that there are significant differences in the relevance of certain indicators, depending on the ESFRI domain of the RI. The indicators therefore need to be adapted to the type of infrastructure. It is therefore proposed that the Strategic Working Groups of ESFRI addressing specific domains should be involved in the tailored development of the monitoring of pan-European RIs.

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 benefits of SC. Qualitative interviews additionally provide broader reflections on certain policy mechanisms that could better motivate Slovenian scientists to scientifically collaborate in the international arena.

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.