We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.

Students’ personal learning networks can be a valuable resource of success in higher education: they offer opportunities for academic and personal support and provide sources of information related to exams or homework. We study the determinants of learning networks using a panel study among university students in their first and second year of study. A long-standing question in social network analysis has been whether the tendency of individuals with similar characteristics to form ties is a result of preferences “choice homophily” or rather selective opportunities “induced homophily”. We expect a latent preference for homophilic learning partnerships with regard to attributes, such as gender, ability, and social origin. We estimate recently developed temporal exponential random graph models to control for previous network structure and study changes in learning ties among students. The results show that especially for males, same-gender partnerships are preferred over heterogeneous ties, while chances for tie formation decrease with the difference in academic ability among students. Social origin is a significant factor in the crosssectional exploration but does appear to be less important in the formation of new (strong) partnerships during the course of studies.

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

Face-to-face interactions in social groups are a central aspect of human social lives. Although the composition of such groups has received ample attention in various fields—e.g., sociology, social psychology, management, and educational science—their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals’ interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms—such as homophily, transitivity, and popularity—to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting.

Genetic risk is particularly salient for families and testing for genetic conditions is necessarily a family-level process. Thus, risk for genetic disease represents a collective stressor shared by family members. According to communal coping theory, families may adapt to such risk vis-a-vis interpersonal exchange of support resources. We propose that communal coping is operationalized through the pattern of supportive relationships observed between family members. In this study, we take a social network perspective to map communal coping mechanisms to their underlying social interactions and include those who declined testing or were not at risk for Lynch Syndrome. Specifically, we examine the exchange of emotional support resources in families at risk of Lynch Syndrome, a dominantly inherited cancer susceptibility syndrome. Our results show that emotional support resources depend on the testing-status of individual family members and are not limited to the bounds of the family. Network members from within and outside the family system are an important coping resource in this patient population. This work illustrates how social network approaches can be used to test structural hypotheses related to communal coping within a broader system and identifies structural features that characterize coping processes in families affected by Lynch Syndrome.

A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other. Personal network structure – the way in which one’s personal contacts know and interact with each other – reflects broader trends in social organization and personal communities, and shapes patterns of social capital, support, and isolation. This article proposes a method to identify typologies of structure in large collections of personal networks. The method is applied to six datasets collected in widely different circumstances and using various survey instruments. It is then compared with another recently introduced method to extract typologies of egocentric network structure. Findings show that personal network structure can be effectively summarized using just three measures of cohesive subgroup characteristics. Structural typologies can then be identified by applying standard cluster analysis techniques to the three variables. Both methods considered in the article capture significant variation in network structures, but they also show substantial levels of disagreement and cross-classification. I discuss similarities and differences between the methods, and potential applications of the proposed typologies to substantive research on personal communities, social support, and social capital.

Measures of bipartite network structure have recently gained attention from network scholars. However, there is currently no measure for identifying key players in two-mode networks. This article proposes measures for identifying key players in bipartite networks. It focuses on two measures: fragmentation and cohesion centrality. It extends the centrality measures to bipartite networks by considering (1) cohesion and fragmentation centrality within a one-mode projection, (2) cross-modal cohesion and fragmentation centrality, where a node in one mode is influential in the one-mode projection of the other mode, and (3) cohesion and fragmentation centrality across the entire bipartite structure. Empirical examples are provided for the Southern Women’s data and on the Ndrangheta mafia data.

This study examines changes in individual social capital during adult life within a 19-year period. Social capital theory and life course theory are combined, and it is argued that changes in social networks do not necessarily go together with changes in social capital: while personal networks are known to decline in size with age, social capital can be expected to accumulate, in particular for those who had a better starting position and therefore more resources to share. Panel data from the survey of the social networks of the Dutch (SSND) (1999–2018) at four points of measurement are employed to inquire into this argument. Social capital is measured by the position generator instrument, and three indicators, that is, resource extensity, mean prestige access, and resource range are analyzed. Results of fixed effect models show that, on average, people maintain access to social capital, and that men and higher educated gain social capital through their life as opposed to women and lower educated. Implications for the understanding of the reproduction of social inequality are discussed. The paper concludes with a reflection upon the value of ego-centered network analysis in the era of big data and data science.

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

This study documents historical trends of size and political diversity in Americans’ discussion networks, which are often seen as important barometers of social and political health. Contrasting findings from data drawn out of a nationally representative survey experiment of 1,055 Americans during the contentious 2016 U.S. presidential election to data arising from 11 national data sets covering nearly three decades, we find that Americans’ core networks are significantly smaller and more politically homogeneous than at any other period. Several methodological artifacts seem unlikely to account for the effect. We show that in this period, more than before, “important matters” were often framed as political matters, and that this association probably accounts for the smaller networks.

Individuals filling specialized, interdependent organizational roles achieve coordinated task execution through effective communication channels. Such channels enable regular access to information, opportunities, and assistance that may enhance one’s understanding of the task environment. However, the time and effort devoted to maintaining those channels may detract from one’s duties by turning attention away from the task environment. Disrupted task environments increase information requirements, thus creating a dilemma in which individuals must sustain benefits offered by important communication channels and relieve burdens imposed by ineffective channels. Using separable temporal exponential random graph models (STERGMs), this paper examines the relationship between situational awareness (SA) and the propensity to sustain or dissolve preexisting communication channels during 10 disruptive events experienced sequentially by a large, multifaceted military organization during a 2-week training exercise. Results provide limited evidence that increased SA detracts from tie preservation; instead SA begins to predict tie preservation during the second week of the exercise. Patterns of organizational adaptation reveal that, over time, improvised coordinative roles increasingly fall upon those with elevated SA. These results suggest that over successive disruptions, the benefits of information provided by communication channels within interdependent, role-specialized organizations begin to outweigh the costs of sustaining those channels.

The paper explores the relationship between personality characteristics, sense of community and the structure of personal networks. We collected personal network data from 100 adults, consisting of the information about their 45 alters and ties among them. In addition to the typically used bivariate correlations to analyze the relationship between individual psychological differences and network structure, we propose first the use of typologies of networks and personality and second modified versions of the triadic census in ego-networks. The results show that different personality types tend to occupy a different kind of networks and that newly introduced triadic measures show a relatively higher association with examined psychological attributes than global network measures. Overall, the Psychological Sense of Community showed higher associations with network measures than Big Five personality traits. The former was positively correlated with measures of closed triads among alters, but also with alters embedded in triads with weak ties. Regarding personality traits, Emotional Stability was positively correlated with strong closed triads and with the overall indicator of density. The elaboration of typologies and the census of triads are shown to be effective strategies for the description of personal networks, as well as for the analysis of individual psychological differences.

Similar peers are more likely to become friends, but it remains unclear how the combination of multiple characteristics, known as multidimensional similarity, influences friendships. This study aimed to investigate whether similarity in gender (attribute) and bullying or victimization (network position) contributes to friendships. The school-level networks of friendships and victim-bully relationships in 17 Dutch elementary schools (2,130 students) were examined using multiplex longitudinal social network models (RSiena). The results showed that friendships were more likely to occur between same-gender peers and between bullies sharing their targets of victimization. Multidimensional similarity (similarities in gender as well as bullying) increased the likelihood of friendships for same-gender bullies targeting the same victims, but not for same-gender victims sharing bullies. The findings underline the importance of unraveling the interplay between different dimensions of similarity for children’s relationships and surpass unidimensional similarity based on single attributes.

Name generators (NGs) and position generators (PGs) have been used to measure resources embedded in personal relationships, namely social support and social capital, respectively. Comparisons of these measures adopted NGs that only elicit a small number of alters (max. 5). In this paper we explore whether the measurement of social capital with NGs eliciting larger personal networks (say 15 to 20 alters) gives more comparable results to the PG in terms of occupational prestige. To address this issue, we designed a personal network questionnaire that combined a multiple name generator (MNG) and a PG and enquired about alter characteristics and alter-alter ties for the two sets of nominations simultaneously, allowing their integrated analysis. The questionnaire was implemented in the software EgoNet to collect data from social/environmental entrepreneurs in Spain (N = 30) and Mexico (N = 30. The analysis shows that the two approaches capture mostly non-overlapping sets of personal network members, suggesting that the PG measured in this case available, but not accessed social capital. Remarkably the NG led to a higher average prestige for this occupational group than the PG, but also a lower heterogeneity in prestige. The consequences of using one or another approach and their interpretations are discussed.

A principled approach to understand networks is to formulate generative models and infer their parameters from given network data. Due to the scarcity of data in the form of multiple networks that have evolved from the same process, generative models are typically formulated to learn parameters from a single network observation, hence ignoring the natural variability of the “true” process. In this paper, we highlight the importance of variability in evaluating generative models and present two ways of quantifying the variability for a finite set of networks. The first evaluation scheme compares the statistical properties of networks in a dissimilarity space, while the other relies on data-driven entropy measures to compute variability in network populations. Using these measures, we evaluate the ability of four generative models to synthesize networks that capture the variability of the “true” process. Our empirical analysis suggests that generative models fitted for a single network observation fail to capture the variability in the network population. Our work highlights the need for rethinking the way we evaluate the goodness-of-fit of new and existing network models and devising models that are capable of matching the variability of network populations when available.

Ego networks are thought to be influenced by the opportunities provided to associate with others given by our master statuses (e.g., race or sex), by the preferences individuals possess for interaction given our personality traits (e.g., extroverted or neurotic), and by the capacity to manage interactions on an ongoing basis given our cognitive ability to recall network information. However, prior research has been unable to examine all three classes of predictors concurrently. We rectify this deficiency in the literature by using a novel dataset of nearly 1000 respondents collected using controlled laboratory designs; using this dataset, we can simultaneously examine the impact of master statuses, personality traits, and social cognitive competencies on ego network size, structure (i.e., density), and composition (i.e., diversity). We find that all classes of predictors influence our ego networks, though in different ways, and point to new avenues for research into human sociability.

R-MAT (for Recursive MATrix) is a simple, widely used model for generating graphs with a power law degree distribution, a small diameter, and communitys structure. It is particularly attractive for generating very large graphs because edges can be generated independently by an arbitrary number of processors. However, current R-MAT generators need time logarithmic in the number of nodes for generating an edge— constant time for generating one bit at a time for node IDs of the connected nodes. We achieve constant time per edge by precomputing pieces of node IDs of logarithmic length. Using an alias table data structure, these pieces can then be sampled in constant time. This simple technique leads to practical improvements by an order of magnitude. This further pushes the limits of attainable graph size and makes generation overhead negligible in most situations.