Explore the vast range of titles available.

Node centrality is one of the most frequently revisited network theoretical concepts, which got many calculation method alternatives, each of them being conceived on different empirical or theoretical network abstractions. The vast majority of centrality measures produced up to date were conceived on static network abstractions (the so-called “snapshot” networks), which arguably are less realistic than dynamic (temporal) network abstractions. The new, temporal node centrality measure that we offer with this article, is based on an uncommon abstraction, of a space-time network derived from service schedules (timetables). The proposed measure was designed to rank nodes of a space-time network based on their spread or transmission potential, and was subsequently implemented on the network of sea ferry transportation derived from the aggregated schedules for sea ferry liner shipping services in Europe, as they occurred in the month of August, 2015. The main feature of our measure, named “the Spread Potential”, is the evaluation of the potential of a node in the network for transmitting disease, information (e.g. rumours or false news), as well as other phenomena, whichever support a space-time network abstraction from regular and scheduled services with some known carrying capacities. Such abstractions are, for instance, of the transportation networks (e.g. of airline or maritime shipping or the wider logistics (delivery) networks), networks of medical (hospital) services, educational (teaching) services, and virtually, of any other scheduled networked phenomenon. The article also offers the perspectives of the measure’s applicability on the non-scheduled space-time network abstractions.

This paper explores the emerging potential of large language models (LLMs) and generative AI for social network analysis (SNA) based on open-ended survey data as a source. We introduce a novel methodology, Survey-to-Multilayer Network (SURVEY2MLN), which systematically transforms qualitative survey responses into structured multilayer social networks. The proposed approach integrates prompt engineering with LLM-based text interpretation to extract entities and infer relationships, formalizing them as distinct network layers representing research similarity, communication, and organizational affiliation. The SURVEY2MLN methodology is defined through six phases, including data preprocessing, prompt-based extraction, network construction, integration, analysis, and validation. We demonstrate its application through a real-world case study within an academic department, where prompt engineering was used to extract and model relational data from narrative responses. The resulting multilayer network reveals both explicit and latent social structures that are not accessible through conventional survey techniques. Our results show that LLMs can serve as effective tools for deriving sociograms from free-form text and highlight the potential of AI-driven methods to advance SNA into new, text-rich domains of inquiry.

Statistics Netherlands (CBS) has recently developed the whole population network (Person Network) file, based on administrative microdata, which includes over a billion interpersonal relationships among approximately 17 million inhabitants of the Netherlands, spanning each year from 2009 onward. Additionally, over the past decade, CBS has conducted the annual Social Cohesion and Well-being (SSW) survey, gathering responses from 83,667 representative individuals on various indicators of social cohesion, including social contacts, volunteering, political participation, and trust in others and institutions. In this study, we construct a merged dataset linking the Person Network file and the SSW survey. We examine the associations between social network centrality and 17 indicators of social capital, including a composite index, and further analyze how family and neighbourhood network centrality relate to self-reported contact with family members and neighbours. While some statistically significant relationships are found, particularly for family centrality, the associations are generally weak, suggesting that current network abstractions capture limited aspects of actual social capital. The findings underscore the need for more refined and substantively meaningful network measures to better understand the structure and quality of social interactions.

Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

While migration research is at the peak of its productivity, a substantial gap persists between scientific evidence and policy action. As societal complexity increases, migration theory loses track on the numerous factors of human migration; the information on the most relevant factors affecting human migration (i.e., migration drivers), essential for policy decision-making, are hidden and dispersed across the ever-growing literature. Introducing a novel approach to conducting a literature review, emphasizing an unbiased selection of literature and the approach to analysing literature by coding, we collect evidence on the most pertinent migration factors. The study establishes a methodology for a quick but rigorous, collaborative gathering of evidence, as well as an initial inventory and an interactive map of nearly 200 factors working at different migration corridors.

Migration, and urbanization as its consequence, is among the most intricate political and scientific topics, predicted to have huge effects on human lives in the near future. Thus being said, previous works have mainly focused on international migration, and the research on internal migration outside of the US is scarce, and in the case of Europe—the ubiquitous center of migration affairs—only in its infancy. Observing migration between settlements, especially using network analysis indicators and models, can help to explain and predict migration, as well as urbanization originating from internal migration. We therefore conducted a network analysis of internal migration in Croatia, providing insights into the size of internal migration in population, and relative sizes between intra-settlement migration, inter-settlement migration and population. Through centrality analysis, we provide insights into hierarchy of importance, especially, in terms of the overall flow and overall attractiveness of particular settlements in the network. The analysis of the network structure reveals high presence of reciprocity and thus the importance of internal migration to urbanization, as well as the systematic abandonment of large cities in the east of the country. The application of three different community detection algorithms provides insights for the policy domain in terms of the compatibility of the current country administrative subdivision schemes and the subdivision implied by migration patterns. For network scholars, the analysis at hand reveals the status quo in applied network analysis to migration, the works published, the measures used, and potential metrics outside those applied which may be used to better explain and predict the intricate phenomenon of human migration.

Migration's influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study delves into two distinctive substrates of Migration Studies: the \"why\" substrate, which deals with identifying the factors driving migration relying primarily on regression modeling, encompassing economic, demographic, geographic, cultural, political, and other variables; and the \"how\" substrate, which focuses on identifying migration flows and patterns, drawing from Network Science tools and visualization techniques to depict complex migration networks. Despite the growing percentage of Network Science studies in migration, the explanations of the identified network traits remain very scarce, highlighting the detachment between the two research substrates. Our study includes real-world network analyses of human migration across different geographical levels: city, country, and global. We examine inter-district migration in Vienna at the city level, review internal migration networks in Austria and Croatia at the country level, and analyze migration exchange between Croatia and the world at the global level. By comparing network structures, we demonstrate how distinct network traits impact regression modeling. This work not only uncovers migration network patterns in previously unexplored areas but also presents a comprehensive overview of recent research, highlighting gaps in each field and their interconnectedness. Our contribution offers suggestions for integrating both fields to enhance methodological rigor and support future research.