Networks and geography: Modelling community network structures as the outcome of both spatial and network processes

This paper focuses on how to extend the exponential random graph models to take into account the geographical embeddedness of individuals in modelling social networks. We develop a hierarchical set of nested models for spatially embedded social networks, in which, following Butts (2002), an interaction function between tie probability and Euclidean distance between nodes is introduced. The models are illustrated by an empirical example from a study of the role of social networks in understanding spatial clustering in unemployment in Australia. The analysis suggests that a spatial effect cannot solely explain the emergence of organised network structure and it is necessary to include both spatial and endogenous network effects in the model.     

A comparative study of social network models: Network evolution models and nodal attribute models

This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature. The models fall into two categories: those in which the addition of new links is dependent on the (typically local) network structure (network evolution models, NEMs), and those in which links are generated based only on nodal attributes (nodal attribute models, NAMs). An exponential random graph model (ERGM) with structural dependencies is included for comparison. We fit models from each of these categories to two empirical acquaintance networks with respect to basic network properties. We compare higher order structures in the resulting networks with those in the data, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). We find that the nodal attribute models successfully produce assortative networks and very clear community structure. However, they generate unrealistic clustering spectra and peaked degree distributions that do not match empirical data on large social networks. On the other hand, many of the network evolution models produce degree distributions and clustering spectra that agree more closely with data. They also generate assortative networks and community structure, although often not to the same extent as in the data. The ERGM model, which turned out to be near-degenerate in the parameter region best fitting our data, produces the weakest community structure.     

Demographic and HIV status diversities as mechanisms of social integration and segregation among Black sexual and gender minorities enrolled in a community-based social network intervention

Drawing on a social integration and intersectionality framework, this study advances a dynamic network understanding of the mechanisms that enable differential patterns of within-group social integration and segregation among Black sexual and gender minorities (BSGM). Specifically, in a cohort of BSGM (18–35 years of age, n = 340) participating in a community-based network intervention for HIV prevention, we examine how sexual, gender, age, and HIV status diversities contribute to friendship formation and maintenance patterns over the 12-month study enrollment period. We found attenuated social integration (or social activity) among non-gay-identified and older BSGM and evidence of social segregation (or homophily) on the basis of sexual identity and age similarities. Accounting for the moderating effects of the intervention revealed that the attenuated integration of non-gay-identified and older BSGM were stronger for participants who received the peer leadership training, and integration challenges were also found for transgender BSGM who received the peer leadership training. Meanwhile, BSGM living with HIV who received the peer leadership training were significantly more integrated than their counterparts in the control arm. These findings help us understand the complicated social fabric among BSGM and the dynamics that interventions for this community may have to contend with or alter.     

A comparison of email networks and off-line social networks: A study of a medium-sized bank

Recently there has been a surge in the availability of online data concerning the connections between people, and these online data are now widely used to map the social structure of communities. There has been little research, however, on how these new types of relational data correspond to classical measures of social networks. To fill this gap, we contrast the structure of an email network with the underlying friendship, communication, and advice seeking networks. Our study is an explorative case study of a bank, and our data contains emails among employees and a survey of the ego networks of the employees. Through calculating correlations with QAP standard errors and estimating exponential random graph (ERG) models, we find that although the email network is related to the survey-based social networks, email networks are also significantly different: while off-line social networks are strongly shaped by gender, tenure, and hierarchical boundaries, the role of these boundaries are much weaker in the email network.     

Statistical power of the social network autocorrelation model

The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model.     

Socio-material network analysis: A mixed method study of five European artistic collectives

In this paper, I argue that we can better understand the relationship between social structure and materiality by combining qualitative analysis of practices in shared physical space with statistical analysis. Drawing on the two-mode approach, I treat social and material structures together with the relationship between them as a two-level socio-material network. In a mixed method study, formalized ethnographic data on such networks in five European artistic collectives are subjected to multilevel exponential random graph modelling. It sheds light on how different kinds of interpersonal ties condition the engagement of individuals with similar materiality over time.     

Information seeking in secondary schools: A multilevel network approach

In this study, we investigate information seeking interactions in secondary schools from a multilevel network approach. Based on network-related theories, we examine the facilitating role of formal subunits. We apply exponential random graph models for multilevel networks and summarize our findings by using a meta-analysis technique. Our results indicate that formal subunits (e.g. subject departments) can, to some extent, facilitate interactions, in loosely coupled organizations (e.g. secondary schools). Finally, this study shows that a multilevel network approach can provide a more informative representation of information seeking ties in knowledge-intensive organizations.     

Friends in locked places: An investigation of prison inmate network structure

The current study investigates informal social structure among prison inmates. Data come from the Prison Inmate Network Study (PINS), a project focused on a unit of a Pennsylvania medium security men’s prison. We focus on 205 inmates and their “get along with” network – an approximation of friendship in other settings. We find a weak subgroup structure dominated by two groups of “old heads” and characterized by moderate (non gang-based) race/ethnic clustering. Structurally, the network resembles adolescents in schools, suggesting that prison inmates are capable of successfully building peer associations. We conclude that under the right conditions self-organizing inmate society can foster social integration reminiscent of other social settings.     

Cross-national variation in political network size,distribution, and prediction

Network size has a fundamental influence on other network properties. As studies of social network size have accumulated beyond the U.S. and Western Europe, diversity in the networks examined and methods used to construct size estimates have hindered the ability to make direct cross-national comparisons. We employ data from a summary political discussion network size measure in 17 surveys conducted in 15 countries between 2012 and 2018. We offer improved cross-national data on political network size distributions and estimate aggregate and country-specific models to understand the factors, both social and political, that predict political network size within countries.     

Exploring the stability of communication network metrics in a dynamic nursing context

Network stability is of increasing interest to researchers as they try to understand the dynamic processes by which social networks form and evolve. Because hospital patient care units (PCUs) need flexibility to adapt to environmental changes (Vardaman et al., 2012), their networks are unlikely to be uniformly stable and will evolve over time. This study aimed to identify a metric (or set of metrics) sufficiently stable to apply to PCU staff information sharing and advice seeking communication networks over time. Using Coefficient of Variation, we assessed both Across Time Stability (ATS) and Global Stability over four data collection times (Baseline and 1, 4, and 7 months later). When metrics were stable using both methods, we considered them “super stable.” Nine metrics met that criterion (Node Set Size, Average Distance, Clustering Coefficient, Density, Weighted Density, Diffusion, Total Degree Centrality, Betweenness Centrality, and Eigenvector Centrality). Unstable metrics included Hierarchy, Fragmentation, Isolate Count, and Clique Count. We also examined the effect of staff members’ confidence in the information obtained from other staff members. When confidence was high, the “super stable” metrics remained “super stable,” but when low, none of the “super stable” metrics persisted as “super stable.” Our results suggest that nursing units represent what Barker (1968) termed dynamic behavior settings in which, as is typical, multiple nursing staff must constantly adjust to various circumstances, primarily through communication (e.g., discussing patient care or requesting advice on providing patient care), to preserve the functional integrity (i.e., ability to meet patient care goals) of the units, thus producing the observed stability over time of nine network metrics. The observed metric stability provides support for using network analysis to study communication patterns in dynamic behavior settings such as PCUs.     

Multiplexity and strategic alliances: The relational embeddedness of coalitions in social movement organisational fields

While scholars have embraced the notion of social movements as networks, there has been little empirical exploration of the emergence of coalitions within these multilayered systems. Here I explore the role of overlapping relations in alliance formation amongst a group of 55 health-related professional social movement organisations mobilised against austerity. Using cross-sectional bivariate exponential random graph models, I find dependencies between digital proxies for alliance, shared allies, information exchange, positive nomination and offline colobbying activity at the dyadic, degree and triadic levels. Cross-network associations indicate that multiplexity plays a non-trivial role in the formation of alliances and, more generally, social movement organisational fields, necessitating increased attention from scholars of social movements.     

Multilevel meta network analysis with application to studying network dynamics of network interventions

In this paper, I introduce new methods for multilevel meta network analysis. The new methods can combine results from multiple network models, assess the effects of predictors at network or higher levels and account for both within- and cross-network correlations of the parameters in the network models. To demonstrate the new methods, I studied network dynamics of a smoking prevention intervention that was implemented in 76 classes of six middle schools in China. The results show that as compared to random intervention (i.e., that targets random students), smokers’ popularity was significantly reduced in the classes with network interventions (i.e., those target central students or students with their friends together). The findings highlight the importance of examining network outcomes in evaluating social and health interventions, the role of social selection in managing social influence, and the potential of using network methods to design more effective interventions.     

Conflict dynamics in collaborative knowledge production. A study of network gatekeeping on Wikipedia

Digitalization has fundamentally changed gatekeeping by allowing users to participate in processes of information control. We study gatekeeping on Wikipedia by analyzing networks of deletions among editors. Factors considered to explain the emergence of these networks are editors’ information production capability, their range of activity, their communicative ties, their geographical location, and their past interactions. Relying on relational event models, we find that the observed gatekeeping processes are not only shaped by the factors related to an editor’s standing in the community, but also to a certain extent by their attribution to content-related groups and some forms of past interactions.     

Review of the effects of Five Factor Model personality traits on network structures and perceptions of structure

We review recent research on Five Factor Model personality and social network analysis to assess how structures develop and are perceived. Extraversion and agreeableness relate consistently to personal but not workplace networks. Extraverts are more likely to seek connections, whereas agreeable individuals receive connections from others. Openness predicts network diversity and is marginally related to position when groups pursue collective goals. Conscientiousness is associated with maintaining certain personal relationships, but is strongly related to central positions in workplace networks. Neuroticism has no consistent relationship with network size or composition, and is differentially related to network positions, depending on the context.     

A sexual affiliation network of swingers,heterosexuals practicing risk behaviours that potentiate the spread of sexually transmitted infections: A two-mode approach

Using the example of the sexual affiliation networks of swingers, this paper examines how the analysis of sexual affiliation networks can contribute to the development of sexually transmitted infection (STI) prevention strategies. Two-mode network methodology and ERGMs are applied to describe the structural composition of the affiliation network and analyse attribute effects. Swingers were found to recruit their sex partners through one large, moderately cohesive network component. Swingers who used drugs or had a longer history of swinging tended to frequent websites instead of clubs. This study confirms the relevance of studying sexual affiliation networks and its additional value for STI epidemiology.     

Types of stable network structures based on exhaustive search

Based on the works of Doreian [Doreian, P., 2006. Actor network utilities and network evolution. Social Networks 28, 137–164] and Xie and Cui [Xie, F., Cui, W., 2008b. A note on the paper ‘cost range and the stable network structures’. Social Networks 30, 102–103], the paper applies exhaustive search and inductive approaches to analyze possible transitions among isomeric social networks with five vertices. A total of seven types of stable network structures are identified, which include the complete, star, chain, cycle, near-chain, near-cycle and null graphs. Then, a modified theorem for the equilibrium forms which corrects the previous work is presented.     

Predictors and outcomes of social network compositions: A compositional structural equation modeling approach

Proportions of a total, including social network compositions (proportions of partner, family, friends, etc.) lie in a restricted space, which challenges statistical analysis. Network compositions can be both dependent and explanatory variables and are usually measured with error by survey instruments. Structural equation models make it possible to correct measurement error bias. Coenders et al. (2011) fitted a factor analysis model to transformed network compositions. In this article, we use another transformation called an isometric log-ratio and we extend the model to include predictors and outcomes. The findings and hypotheses in the literature can be reformulated with isometric log-ratios in a more interpretable manner. For instance, we find relationships of gender with partner support, of education and extraversion with friend support, and of family support with tie multiplexity and closeness.     

Being close: The quality of social relationships in a local organic cereal and bread network in Lower Austria

Experience of the drawbacks of a globalised and industrialised food system has generated interest in localised food systems. Local food networks are regarded as more sustainable food provision systems since they are assumed to have high levels of social embeddedness and relations of regard. This paper explores the social relations between food actors and how ‘local’ and ‘organic’ are expressed by detailing how actors describe qualities of their intra-network relationships, how they understand ‘local’ and how they are connected within the food system. A study from the province of Lower Austria in Austria, where organic cereals and bread are produced and marketed, serves to illuminate these issues. Actors agreed that geographical closeness contributed to the social closeness they experienced and that social relationships were a strong reason for being in the network. However, the meaning of ‘local’ was elastic depending on where inputs and consumers could be found. Furthermore, despite strong commitment to organic production methods and the local market, actors faced constraints that made them hybrids between organic and conventional, and between locally focused and globally dependent. Thus, the binary thinking along the local-global and organic-conventional divide does not hold. While it is important to not make a causal link between high quality of social relationships and local food networks, the case described here indicates the possibility of such a link.     

Social creativity as a function of agent cognition and network properties: A computer model

Inventions – concepts, devices, procedures – are often created by networks of interacting agents in which the agents can be individuals (as in a scientific discipline) or they can themselves be collectives (as in firms interacting in a market). Different collectives create and invent at different rates. It is plausible that the rate of invention is jointly determined by properties of the agents (e.g., their cognitive capacity) and by properties of the network of interactions (e.g., the density of the communication links), but little is known about such two-level interactions. We present an agent-based model of social creativity in which the individual agent captures key features of the human cognitive architecture derived from cognitive psychology, and the interactions are modeled by agents exchanging partial results of their symbolic processing of task information. We investigated the effect of agent and network properties on rates of invention and diffusion in the network via systematic parameter variations. Simulation runs show, among other results, that (a) the simulation exhibits network effects, i.e., the model captures the beneficial effect of collaboration; (b) the density of connections produces diminishing returns in term of the benefits on the invention rate; and (c) limits on the cognitive capacity of the individual agents have the counterintuitive consequence of focusing their efforts. Limitations and relations to other computer simulation models of creative collectives are discussed.