The embedding of social actors within physical spaces has been increasingly studied in Social Network Analysis. What has received less attention to date is a more dynamic perspective on the different kinds of spaces that actors flow through in their everyday lives, which have become more manifold and shifting, for example through hybrid working and digital technologies.

In this talk I want to explore a new perspective for the relationship between people and space by building on Ron Breiger’s seminal approach to the duality of networks, which considered how individuals intersected within groups by virtue of their participation in events. This can be applied to people and spaces. Practically, this means focussing on movement patterns, i.e., how people make their way from A to B, which path they chose, and therefore how often they frequented different spaces along the way.

This approach works across scales, but here two case studies are explored of space usage processes inside buildings: an in-patient ward and the variety of healthcare workers moving through it, and a university building populated by students and staff. Preliminary insights suggest that the particular patterning of the spaces an individual frequents tells us something about the individuality of the actor and their social embeddedness, and vice versa, which people use which spaces may tell us something about the character of those spaces.

This work hopes to widen perspectives on the context of social structures by offering a way of analysing dynamic actor-related behaviours in space.

Background and Aims: Connecting for Care is a mixed-method social network analysis case study of Canada’s child development and rehabilitation network. It explores social ties and knowledge translation (KT) patterns among healthcare providers, families, researchers, and KT support personnel. This presentation outlines our integration of algorithmic and geographic visualization approaches to facilitate analysis and qualitative data gathering.

Methods: A cross-sectional national online survey collected respondents’ KT connections across Canada’s 10 provinces and 3 territories. Using UCINet and NetDraw, we created egocentric maps showing inter/intra-provincial/territorial knowledge exchange patterns. Nodes were positioned manually by province/territory and region using WindowTop software to optimize clarity. These visualizations were presented during interviews to further our understanding of network structure and factors influencing tie development.

Results: We identified 596 connections among 622 individuals (234 respondents; 388 non-respondents). Analysis revealed 452 intra- and 144 inter-provincial/territorial ties, with connections concentrated in densely populated regions. We generated 13 provincial/territorial maps and 3 regional visualizations for interpretation. Average geodesic distance of 3.6 suggests relatively efficient knowledge exchange despite Canada’s vast geography. Knowledge brokers (betweenness centrality range: 0-1003, median: 0) were primarily concentrated in central Canada. Interview participants valued intra-provincial connections for exchanging context-specific information within Canada’s decentralized health system.

Conclusion: The predominance of intra-provincial ties reflects the importance of region-specific expertise and the opportunity to support the implementation of evidence-based practices across Canada. Qualitative inquiry enhanced our understanding of network structure. Software development that improves the integration of geographic and network data would facilitate this approach.

While migration patterns have traditionally been analyzed primarily through economic, security, and migrant community network frameworks, the growing impact of climate change necessitates a shift in focus to understand the role of climate factors in shaping these movements. This study explores the intersection of climate change and global migration patterns over the past two decades, focusing on South-to-North migration. Utilizing international migration data and satellite imagery, we employ valued Exponential Random Graph Models and spatial analyses to identify emerging migration hotspots linked to regions experiencing severe climate impacts. These geocoded migration networks highlight how climate stressors, such as floods, sea level rise (elevation change), and extreme temperatures, influence the magnitude and direction of migration flows. Our findings reveal significant shifts from traditional migration patterns, emphasizing how climate-induced stressors reshape the relational dynamics and migration pathways of these networks. By focusing on the spatial dimensions of climate-induced mobility, this study underscores the importance of geographic and climatic context in understanding migration networks. This study contributes to a more nuanced understanding of how environmental stressors shape network structures and offers valuable insights for understanding human mobility and fostering social cohesion across borders in a climate-impacted world.

The rapid deployment of green innovation is critical to achieving global carbon targets. Climate change mitigation technologies (CCMTs) produce a global public good, yet their R&D costs, risks, and benefits remain strikingly uneven. Over the past two decades, CCMT co-invention networks have evolved from core-periphery structures—dominated by the U.S., China, and Germany, into more polycentric systems where emerging economies like India play an increasing role (Ma et al., 2022). However, little is known about how these co-invention networks and their dynamics influence patent filing strategies and jurisdiction selection, and so their diffusion across the global market. This study leverages global patent data from PATSTAT, USPTO, and Lens.org, applying a relational event model to analyse the structural drivers of CCMT patenting decisions. We examine how inventor nationality, cross-country collaborations, and network topology (e.g., hub-spoke vs. distributed structures) shape patent jurisdiction choices. By unpacking the interplay between global co-invention networks and patenting decisions, this research offers insights into the strategic dimensions of green technology diffusion, market selection, and innovation drivers.

The emergence of online social networks like Facebook in the early 2000’s promised a breakthrough in social networks research social network research by enabling analysis of societal-scale interactions due to abundant data. However, despite many groundbreaking studies, progress has been limited by a lack of freely available data. In rare cases where such data have been made available by platforms to researchers, individual-level data can typically not be shared with the wider research community. However, data that are aggregated to higher social entities, such as municipalities, can be shared more easily. This paper presents one such data set based on the (now defunct) Dutch social network platform Hyves. From an underlying individual-level network covering a significant fraction of the population, we create a data set of consisting of topological features of within-municipality networks, covering all municipalities in the Netherlands. This provides a unique insight into features of social connectivity within municipalities that is not readily available from other resources. We present descriptives of topological features of municipality networks, explore associations with other properties of municipalities, and demonstrate the usefulness of municipality-level network measures for social research.

Migration as a network, while not a novel idea, has recently gained significant attention for analyzing both international and internal migration patterns (Danchev & Porter, 2021; Pitoski et al., 2021a). However, despite its potential, network analysis has often been applied to migration with insufficient attention to interpretability(Pitoski et al., 2021a), focusing on estimating network metrics and tools, such as community detection algorithms, even when they are conceptually inadequate. Furthermore, the lack of comprehensive migration flow data has impeded effective network analysis. When flow data is available, assigning weights to ties remains a challenge, with proposed solutions often inadequate—for instance, simply taking the number of migrants moving from one place to the other (Abramski et al., 2020; Chen et al., 2021; Fagiolo & Mastrorillo, 2013; Pitoski et al., 2021c, 2021b; Zhang et al., 2020), or binarizing flows based on arbitrary thresholds (Carvalho & Charles-Edwards, 2020; Peres et al., 2016). Rarely has weight attribution accounted for origin and destination population sizes, and never for overall migration levels or all three factors simultaneously.

Adequately measuring and interpreting migration as a network is crucial, as the conceptual framework of migration networks offers exciting opportunities for advancing migration research. By merging classical migration theories with long-theorized systemic approaches (Mabogunje, 1970), deeper insights into migration phenomena can emerge, particularly in the context of internal migration, where the structure of the migration system is less shaped by political factors. The relatively better availability of flow data for internal migration also paves the way for robust longitudinal analysis of migration networks. In recent years, systemic analyses exploring changes in the structure and interconnectivity of internal migration have gained traction (DeWaard et al., 2020; Huang & Butts, 2023), particularly in response to declining migration intensity in some Western countries—trends that have proven difficult to explain, especially with bilateral-level analyses. Moreover, major societal shifts (e.g., the COVID-19 pandemic and the rise of remote work) may have long-term implications for the (re)configuration of migration systems. However, realizing these analytical promises depends on adequately measuring and transposing migration systems into networks.

This paper proposes a novel method for rescaling migration networks that neutralizes the effects of population size and yearly migration fluctuations while ensuring that edge weights accurately reflect the attractiveness between geographical areas. This approach enhances the overall interpretability of the network and enables the use of network tools that are otherwise difficult to apply when edges are improperly weighted. Notably, it facilitates the identification of repulsive ties, which are critical for a more comprehensive understanding of migration systems and improves the validity of community detection. It also enables the calculation of network metrics that were previously inapplicable to migration systems (e.g., density) or had limited informative value (e.g., certain centrality measures), providing more direct, intuitive, and meaningful interpretations. As an example, we will apply this method to Canadian migration data to demonstrate its potential for network-based migration analysis.

Urban studies have shown that youth living in disadvantaged and remote areas are less likely to have relationships to people from different social backgrounds and milieus, sometimes called bridging social capital. This so-called neighbourhood effect may restrict their life opportunities. However, existing evidence is largely based on the area where young people (the ‘egos’) live and often ignores the personal (egocentric) network context and where the individuals connected to the young people (their ‘alters’) live, possibly leading to misattribution errors. This paper aims to bridge this gap by examining how the spatial patterns of young adults’ personal networks relate to social capital. The data come from the 2020-23 Swiss Federal Survey of Adolescents (www.chx.ch) on personal networks of young adults. It includes the almost complete national cohort of young Swiss men (N approx. = 60,000) aged between 18 and 21 years, and a sample of about 15,000 Swiss women aged 19. Based on data from this large-scale representative national youth survey, we first develop a typology of four distinct spatial patterns based on how alters are clustered in and scattered across places defined as Swiss (or transnational) employment areas and foreign locations. We then analyse how these patterns are related to area deprivation where young people reside and social capital, measured in terms of the size, composition (homophily) and structure of emotional support and conflict ties between young people and the members of their personal networks.

Spatial isolation approaches to racial disparities have long assumed that lack of access to well-resourced communities is a critical mechanism through which racial segregation limits the wellbeing and life chances of Black youth. Recent research indicates that Black youth spend a substantial amount of time outside of segregated Black spaces due both to residence and everyday routines. Although Black youth may benefit from access to organizational and amenity resources in advantaged neighborhoods, they may also experience elevated exposure to the risk of scrutiny, microaggression, and discrimination. To date, few quantitative studies have addressed the emotional experience of Black youth as they navigate socioeconomically advantaged urban areas, particularly youth in groups that include Black males – potentially drawing greater negative scrutiny from the local environment.

We draw on data from the 2014-16 Adolescent Health and Development in Context study to examine the in situ emotional responses of Black youth to spending time in advantaged neighborhoods. The AHDC uses a network name generator to collect up to 10 non-household friends or other people with which the youth spends the most time during a typical week. Youth participants report on age, race, sex, education, behaviors, risk behaviors, and relationship attributes for each named partner. After an initial in-home survey, youth participants are given a smartphone to complete a 7-day geographically-explicit ecological momentary assessment (EMA) that collects continuous GPS data along with smartphone-based brief surveys. The EMA module administers five daily, randomly-timed prompts to complete a mini-survey that collects current information on the presence of household and network partners, affect, activities, risk behaviors, and perceptions of the social climate of the current location at the time of the prompt. By asking which specific household and network partners are present in the moment, EMA responses can be linked to characteristics of the network partners present as well as aspects of the spatial environment.

Our dependent variables are youths’ EMA ratings of negative and positive affect. Youth report agreement at the time of the prompt on a scale from 1 to 5 with 1 being “not at all” and 5 being “extremely” to these 7 items: daring, happy, confident, relaxed, excited, cheerful, and energetic (Cronbach’s alpha = .86). Negative affect includes 9 items: angry, sad, stressed, bored, lonely, afraid, rejected, nervous, and irritable (Cronbach’s alpha = .80). The presence of Black friends that are male is a count of named network partners reported as present at the time of the EMA prompt, constructed as categories of 0 (reference), 1, 2, and 3 or more Black male friends present.

OLS regression models of emotional outcomes with youth-level fixed effects (N=1727 EMAs nested within 406 Black youth) offer evidence that the affluence level of the immediate neighborhood environment is negatively associated with positive mood for Black youth when accompanied by multiple Black male youth. We discuss the implications of everyday routine location effects on mood for understanding racial disparities in mental health and wellbeing as well as the broader benefits of incorporating network data into EMA data collection approaches.

Over the last few decades, the number of shops offering an alternative to the traditional supermarket model has grown in France. One of this shop's aims is to create social links between these consumers. The way the shop operates (participative or not), its clientele (homogeneous or heterogeneous), its place in the neighborhood, or the layout of the premises can be favorable or unfavorable to interaction between consumers.

What is the influence of the type of shop on the consumers’ interaction network structures and dynamics?

A non-participant observation protocol with an observation grid and a field notebook was set up on 3 sites: an organic franchise shop, a solidarity grocery shop and a cooperative supermarket. The grid is used to systematically record consumers’ attributes and their interactions in the shops to build a network per observation (25 sessions so far).

The first results show that the cashiers are at the center of the 3 networks, which is consistent with the way a shop operates. The organic store network has the biggest number of nodes (677) but the lowest density (1,6%) and 70% of interactions are between employees and customers. Only 4 edges (1,7%) are between customers who did not enter the shop together. In contrast, the solidarity grocery shop has a denser network (29%). Finally, the cooperative supermarket network has more interaction with cashiers than with other customers, but their exchanges are richer (edge weight of 2,43 out of 3) because the customer and the cashier are both members of the cooperative.

Over the past several years, sexual abuse scandals have erupted across organizations ranging from universities to workplaces to places of worship. Despite arrests and monetary payouts to victims, abuse often continued undetected, and covered-up, for decades. In this presentation, I argue that organizations systematically conceal misconduct by strategically moving workers accused of abuse to locations that are geographically distant and socially marginalized. I investigate this phenomenon by quantitatively examining the relocation of Catholic priests credibly accused of child sexual abuse within the Archdiocese of Boston. In the broader Catholic Church, movement of priests - the “geographic solution” - is a well-documented response to abuse allegations (Reynolds 2023; Wall 2019). Recent work suggests that dumping grounds for accused priests did not have to be geographically remote as long as they were socially marginalized (Reynolds 2023). It is less well understood how these forms of the “geographic solution” combined to create movement patterns of accused priests and how these patterns differed from those of non-accused priests. I investigate how the geographic and social location of parishes, in combination with the structure of organizational mobility networks, relate to abusive priest relocation. Using novel data collected from the Official Catholic Directory and BishopAccountability.org, I recreate the movement patterns of both non-accused and accused priests in Boston from 1930-1990 and convert them into networks in which the nodes are geocoded parishes and the edges are priest movements among them. I examine whether the movement of accused priests creates distinct network patterns reflective of strategic relocation, and I compare these movements to those of non-accused priests. I test whether the relocations of abusive priests were skewed towards socially and geographically distant parishes and influenced by the mobility network structure.

Urban highways are common, especially in the US, making cities more car-centric. They promise the annihilation of distance but obstruct pedestrian mobility, thus playing a key role in limiting social interactions locally. Although this limiting role is widely acknowledged in urban studies, the quantitative relationship between urban highways and social ties is barely tested. Here we define a Barrier Score that relates massive, geolocated online social network data to highways in the 50 largest US cities and which captures the connectivity difference between the actual network and a randomized null model. As such, at the unprecedented granularity of individual social ties, we show that urban highways are associated with decreased social connectivity. This barrier effect iis especially strong for short distances and consistent with historical cases of highways that were built to purposefully disrupt or isolate Black neighborhoods. Using multivariate regression models at the census tract level, we confirm that decreased social connectivity related to the presence of highways holds even when accounting for confounding factors such as socio-demographic backgrounds, or other types of barriers. By combining spatial infrastructure with social tie data, our method adds a new dimension to demographic studies of social segregation. Our work can inform reparative planning for an evidence-based reduction of spatial inequality, and more generally, support a better integration of the social fabric in urban planning.

Although neighborhood relations are omnipresent in debates on the intensification of urban segregation in cities, few studies compare them to other types of social relationships and circles (family, friends, professional, associative, etc.), whether they are locally based and/or extend to other areas. However, as Barry Wellman pointed out as early as 1979, taking social networks as a starting point allows us to connect relationships shaped by residential contexts to the overall structure of personal networks analyze their role in network formation, and assess their impact on access to various resources.

The research underlying this presentation studies the spatial dimension of social segregation, using the conceptual and methodological approaches of social network analysis. More specifically, it compares relational contexts with local activity contexts to examine the place and role of neighbourhood relationships in networks to see whether and to what extent these relationships participate in the formation of "entre-soi" (understood as a sociability that is both homogeneous and cohesive), which primarily takes place within shared activities (education, work, etc.). The aim is to expand on the results of a previous questionnaire survey in which we were able to distinguish between two types of local relationships: relationships created with "neighbours" and those formed with what we call "more-than-neighbours", and those beyond the local space. It has also highlighted the importance of activity contexts, - beyond mere geographical proximity (work, friend groups, associations, etc.) - in reinforcing the homogeneity and cohesion of personal networks. However, quantitative data did not allow us to study precisely each of these local relational contexts and how they interact. It also did not distinguish between formal and informal sociability, nor between individual and collective relations.

Based on an ongoing qualitative study conducted in a neighborhood in Toulouse (France), this presentation proposes a more detailed and dynamic analysis of the plurality of activity contexts in which local relationships are embedded, as well as their interconnections. The goal is to understand how these relationships are formed, maintained, and evolve over time, and to what extent they contribute to either social homogeneity or openness. Interviews with residents and discussions based on the visualization of their network have enabled us to draw up sociological portraits revealing different relational dynamics depending on the type of relationship, social circles and also ego characteristics. These first results provide new insights into the interplay between "structural" homogeneity (linked to the neighborhood's social composition) and "elective" homogeneity (driven by affinity-based ties) within personal networks.

The academic research ecosystem has long been shaped by systemic inequities, favoring scholars with access to well-funded institutions, elite networks, and dominant publication platforms. As a result, knowledge production and dissemination remain concentrated among a privileged few, reinforcing epistemic hierarchies and limiting diverse contributions. In an era of geopolitical shifts and the rapid advancement of Generative AI (GenAI), new opportunities emerge to rethink how academic collaboration is structured.

This study suggests to employ a mixed-method approach – combining qualitative insights from interviews with a quantitative social network analysis to identify key structural barriers faced by researchers from underfunded institutions. By mapping these obstacles, we aim to understand how digital inequalities, funding constraints, and AI-driven changes affect academic participation. Based on these findings, we propose BrainSpill, a prototype social network platform designed to foster inclusive, decentralized knowledge exchange. This tool seeks to mitigate existing disparities by leveraging AI-driven matchmaking, open-access collaboration tools, and reputation mechanisms that promote fair participation.

Our work contributes to the discourse on equity in academia by offering actionable solutions to bridge the gap between privileged and underrepresented researchers, ultimately advocating for a more inclusive and globally connected research landscape.

Recent scholarship has increasingly examined global disparities in academic production between central and peripheral regions. Empirical studies highlight inequalities in research impact, Anglophone publishing bias, linguistic barriers, and North-South divides in the generalizability of knowledge claims. On the theoretical and political front, “decolonizing knowledge” has gained traction across disciplines, including sociology, as scholars interrogate the institutional and epistemological underpinnings of these disparities. This study applies insights and tools from social network analysis to examine between-country collaboration networks in sociological publishing. As a first step, I analyze co-authorship patterns in two US-branded but globally prestigious journals—American Sociological Review (ASR) and American Journal of Sociology (AJS) (1966-2018), assessing the geographic distribution of authors and their institutional affiliations. Findings reveal that the U.S. is an absolute star in this network, with nearly all international collaborations involving U.S.-based institutions. Only three countries have ever published independently in ASR/AJS without U.S.-affiliated co-authors, while Austria is the only case of international collaboration without a U.S. tie. Frequent U.S.-linked collaborators include developed countries like Canada, the UK, Germany, and Israel, while Global South countries are largely absent. European nations form the only visible non-U.S. collaboration cluster. As a next step, I will apply Borgatti’s core/periphery model to quantify network centrality of each participating country and extend the analysis to British Journal of Sociology (BJS), Sociology, Journal of Sociology (JoS), Current Sociology (CS), and International Sociology (IS) to assess whether these non-U.S. flagship journals exhibit more globally inclusive patterns.