Random sampling of alters from networks: A promising direction in egocentric network research

The social network perspective has great potential for advancing knowledge of social mechanisms in many fields. However, collecting egocentric (i.e., personal) network data is costly and places a heavy burden on respondents. This is especially true of the task used to elicit information on ties between network members (i.e., alter-alter ties or density matrix), which grows exponentially in length as network size increases. While most existing national surveys circumvent this problem by capping the number of network members that can be named, this strategy has major limitations. Here, we apply random sampling of network members to reduce cost, respondent burden, and error in network studies. We examine the effectiveness and reliability of random sampling in simulated and real-world egocentric network data. We find that in estimating sample/population means of network measures, randomly selecting a small number of network members produces only minor errors, regardless of true network size. For studies that use network measures in regressions, randomly selecting the mean number of network members (e.g., randomly selecting 10 alters when mean network size is 10) is enough to recover estimates of network measures that correlate close to 1 with those of the full sample. We conclude with recommendations for best practices that will make this versatile but resource intensive methodology accessible to a wider group of researchers without sacrificing data quality.     

Introduction to stochastic actor-based models for network dynamics

Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process ‘driven by the actors’, i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes (‘actor covariates’) and of characteristics of pairs of nodes (‘dyadic covariates’). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior.     

Equicentrality and network centralization: A micro–macro linkage

This paper investigates a linkage between micro- and macrostructures as an intrinsic property of social networks. In particular, it examines the linkage between equicentrality [Kang, S.M., 2007. A note on measures of similarity based on centrality. Social Networks 29, 137–142] as a conceptualization of a microstructural process (i.e., the likelihood of social actors to be connected with similarly central others) and network centralization as a macrostructural construct, and shows that they have a negative linear association. In other words, when actors are connected with similarly central alters (i.e., high equicentrality), the overall network centralization is low. Conversely, when highly central actors are connected with low-centrality actors (i.e., low equicentrality), the overall network centralization is high. The relationship between degree equicentrality and degree centralization is more significant in observed networks, especially those evolving over time, as compared to random networks. An application of this property is given by venture capital co-investment networks.     

Motivation for and use of social networking sites: Comparisons among college students with and without histories of non-suicidal self-injury

Objective: This research examines potential differences in social network use and motivation for social network use by non-suicidal self-injury (NSSI) status. Participants: 367 (73% women; M_age = 20.60) college students were recruited in November–December 2011. Methods: A random sample of 2,500 students was accessed through a university registrar to recruit students interested in an online survey assessing NSSI and various health-related behaviors. Results: Social network use and motivations for social networks did not differ by NSSI status. Conclusions: Results suggest that it is not patterns of use or motivation to use social networks that could lead to concern about online behavior (i.e., behavior increasing risk of future NSSI) among those with NSSI history. Rather, future preventive and intervention efforts should address the NSSI-related content that is available online, since this is unregulated, often explicit, and commonly includes “pro-NSSI” content that may be problematic and increase risk among vulnerable individuals.     

Who benefits from network intervention programs? TERGM analysis across ten Philippine low-income communities

Building social capital and strengthening social networks among members of low-income communities has been recommended as a potential pathway out of poverty. However, it is not clear how network-strengthening interventions and community-based programs interact with pre-existing networks and power structures. We examine the impact of one such intervention in ten low income communities in the Philippines. The intervention is a standardized program of a faith-based organization implemented in thousands of communities in multiple countries. It brings together low-income individuals in each community for 16 weekly sessions about health, income generation, and Christian values. An important but yet unmeasured goal of the intervention is the strengthening of social networks among the participants. We measured the social networks before and after the intervention and analysed their changes both separately and jointly for all ten communities with temporal exponential random graph models (TERGM). We modelled the post-intervention network structures conditioning on the pre-intervention networks, pre-intervention node attributes, and attribute changes through the intervention. We found social engagement (measured by social visits to others) to moderate most consistently the effects of the intervention across the ten communities. Those who were more socially engaged consistently strengthened their networks through the intervention. By contrast, some network mechanisms strongly diverged between the communities. In particular, religiosity was positively associated with gaining social links through this faith-based intervention in some communities and negatively in others. Similar communities may in some aspects react to the same intervention in opposite ways—a phenomenon that should be further explored through studies of larger numbers of comparable networks.     

The Psychology of Working in Practice: A Theory of Change for a New Era

This article advances the psychology of working theory by developing a parallel change‐based paradigm to guide interventions at the level of individuals (i.e., the psychology of working counseling) and systems (i.e., the psychology of working systemic intervention). The change paradigm presented in this article includes (a) a needs assessment encompassing survival, social connection/contribution, and self‐determination and (b) input on the mobilization of agentic action that includes critical reflection and action, proactive engagement, social support, and community engagement. The needs assessment and agentic action aspects of this approach can be used to foster change in individual counseling, advocacy, and systemic intervention. Case examples reflecting a systemic intervention and an individual counseling vignette are presented along with implications for research, such as explorations of the impact of these change models on client outcomes and systemic change efforts.     

An introduction to exponential random graph (p) models for social networks

This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph models. Sociological Methodology, in press] offer substantial improvement.     

Prestige in social dilemmas: A network analytic approach to cooperation among Bogotá’s art organizations

What brings about cooperation in social dilemmas? The literature has proposed motivational and strategic solutions to social dilemmas. In motivational solutions, actors cooperate because they care about fairness. In strategic solutions actors cooperate because their chances to exploit others are reduced. This paper argues that actors also cooperate in order to gain prestige. The effect of prestige on cooperation is examined relying on a unique set of network and covariate data on art organizations (museums, independent art spaces, galleries and art foundations) in Bogotá, the capital of Colombia. Bogotá is a not a very likely environment for cooperation, since distrust is still widespread after a long civil war. The temporal exponential random graph models predict dyadic cooperation (i.e., between two actors) based on past and present cooperation in the network as well as of the organizations’ characteristics. The analysis thus controls for the effect of actors’ past and present interaction on the present cooperation dilemma. The paper shows that prestige affects cooperation in social dilemmas independent of past and present interactions.     

Multilevel network interventions: Goals,actions, and outcomes

COVID-19 has resulted in dramatic and widespread social network interventions across the globe, with public health measures such as distancing and isolation key epidemiological responses to minimize transmission. Because these measures affect social interactions between people, the networked structure of daily lives is changed. Such largescale changes to social structures, present simultaneously across many different societies and touching many different people, give renewed significance to the conceptualization of social network interventions. As social network researchers, we need a framework for understanding and describing network interventions consistent with the COVID-19 experience, one that builds on past work but able to cast interventions across a broad societal framework. In this theoretical paper, we extend the conceptualization of social network interventions in these directions. We follow Valente (2012) with a tripartite categorization of interventions but add a multilevel dimension to capture hierarchical aspects that are a key feature of any society and implicit in any network. This multilevel dimension distinguishes goals, actions, and outcomes at different levels, from individuals to the whole of the society. We illustrate this extended taxonomy with a range of COVID-19 public health measures of different types and at multiple levels, and then show how past network intervention research in other domains can also be framed in this way. We discuss what counts as an effective network, an effective intervention, plausible causality, and careful selection and evaluation, as central to a full theory of network interventions.     

Evaluation of school‐based AIDS education curricula in San Francisco

Students in 3 middle (n=385) and 3 high schools (n=254) in the San Francisco Unified School District participated in the evaluation of an AIDS education curriculum. Classes within‐schools were designated as either intervention classes or nonintervention classes. Students in intervention classes received 3 class periods of AIDS instruction with a newly developed curriculum. Students in nonintervention classes did not receive any special AIDS instruction. All students completed a pretest and posttest AIDS knowledge and attitude survey. The results indicated that AIDS instruction classes demonstrated a significant knowledge‐advantage, as well as change in attitudes (e.g., reflecting greater tolerance for attending class with students who may have AIDS or HIV infection).     

Connecting for success: Egocentric network types among underrepresented minority students at college

This study investigates the egocentric networks of underrepresented minority students. Adjustment to college for underrepresented minority students often means balancing between the origin (i.e., low-status family) and host (i.e., academic) environments, requiring students to develop different coping strategies. The analysis included 124 Roma university students. We employed the contact diary method and the EgoNet program for mapping students’ egocentric networks. We explored classification among egocentric networks by cluster analysis based on the network proportion of the origin, fellow, and host groups. Three clusters were distinguished. The Host-based cluster proved to be the most advantageous, while the Balancing cluster appeared to be the most disadvantageous in terms of network resources, well-being, and trust. Partners’ ethnicity and educational attainment showed a strong connection with cluster membership. Our results highlight that small fellow communities can successfully interconnect the origin and host groups in students’ egocentric networks.     

Endogeneity and permeation in an organizational communication network

Face-to-face conversation has been shown to be influenced by features that ensure comprehensible and linear conversation (“endogeneity”), and attributes of its potential participants, such as organizational position or contextual role, that “permeate” these endogenous effects. A direct comparison of both features is rarely studied, however, due to methodological challenges it presents. In addition, conversation is increasingly taking place in new mediated forms, such as emails and social media. It remains to be seen if social dynamics observed in face-to-face conversation transfer to these new media forms. Using an email network among individuals during a military exercise, we use relational event modeling to determine whether social norms of communication dominate conversation flow in this medium and to what extent individual attributes affect the flow of messages to and from those in the organization. Though email communication does not require immediate response, we find that immediate-response turn-taking dynamics occur anyway; though emails are only visible to sender and recipient(s), we observe a “Matthew effect” in the communication sequence (i.e., those who receive many emails receive even more in the future). Finally, these dynamics are only marginally affected by measured individual attributes (e.g., military rank, situational awareness), suggesting the primacy of “endogenous” conversation dynamics in this network. We thus find that many previously-observed social dynamics in face-to-face conversation carry over to the new form, despite practical differences between the two types. This study is only one of a handful that directly compares endogeneity and permeation in a communication network.     

Seeing things clearly: social structure,personality, and accuracy in social network perception

People differ in their ability to perceive accurately the informal patterns of interpersonal relationships in their social groups, i.e., the group's network structure. Existing research leaves us largely unable to explain the variation in accuracy in social network perception. This paper argues that the study of accuracy in social network perception should consider the effect of both situational factors and individual differences. To explore this claim, three research centers in an Italian university were studied. Perceptions of the friendship and the work-related advice networks in the organization were compared to the actual network. The results indicate that both an observer's position in the formal and informal social structure of the organization, and his or her personality traits contribute to determining accuracy in social network perception.     

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.     

An algorithm and metric for network decomposition from similarity matrices: Application to positional analysis

We present an algorithm for decomposing a social network into an optimal number of structurally equivalent classes. The k-means method is used to determine the best decomposition of the social network for various numbers of subgroups. The best number of subgroups into which to decompose a network is determined by minimizing the intra-cluster variance of similarity subject to the constraint that the improvement in going to more subgroups is better than a random network would achieve. We also describe a decomposability metric that assesses how closely the derived decomposition approaches an ideal network having only structurally equivalent classes.     

Variations in network boundary and type: A study of adolescent peer influences

This study compares variation in network boundary and network type on network indicators such as degree and estimates of social influences on adolescent substance use. We compare associations between individual use and peer use of tobacco and alcohol when network boundary (e.g., classroom, entire grade in school, and community) and relational type (elicited by asking whom students: (a) are friends with, (b) admire, (c) think will succeed, (d) would like to have a romantic relationship with, and (e) think are popular) are varied. Additionally, we estimate Exponential Random Graph Models (ERGMs) for 232 networks to obtain a homophily estimate for smoking and drinking. Data were collected from a cross-sectional sample of 1707 adolescents in five high schools in one school district in Los Angeles, CA. Results of logistic regression models show that associations were strongest when the boundary condition was least constrained and that associations were stronger for friendship networks than for other ones. Additionally, ERGM estimations show that grade-level friendship networks returned significant homophily effects more frequently than the classroom networks. This study validates existing theoretical approaches to the network study of social influence as well as ways to estimate them. We recommend researchers use as broad a boundary as possible when collecting network data, but observe that for some research purposes more narrow boundaries may be preferred.     

Limited opportunities: Adolescents’ access to social capital in secondary schools in three European countries

While previous research indicates that students benefit from their peers’ resources, little is known about access to social capital in the school context. Therefore, this study examines differential access to social capital – measured by friends’ socioeconomic status (SES), the number of books they have at home, and their reading habits – in secondary schools in Germany, the Netherlands, and Sweden. Relying on a large-scale dataset, I investigate the association between socioeconomic status, minority status, and social capital using complete friendship network information. I argue that social capital access is connected to a two-stage process consisting of school sorting and friendship selection. To differentiate between these two processes, I apply within-between random effects (REWB). The models show that friendship selection is much less relevant for access to social capital than school sorting. Results indicate that while high-SES students have better access to social capital across dimensions, access patterns for minority students are more nuanced.     

Adolescent network positions and memory performance in adulthood: Evidence from sibling fixed effects models with sociometric network data

Although research has explored social factors influencing memory performance during adolescence, the impact of adolescent social network positions remains largely unknown. This study examines whether adolescent network position is associated with memory performance in adulthood, while also considering potential gender differences. The study used a sibling sample from the National Longitudinal Study of Adolescent to Adult Health (N = 2462) and employed sibling fixed effects models to account for unobserved family background factors, such as genetics, parental characteristics, family environment, and childhood neighborhood. Four dimensions of adolescent network position—i.e., popularity, sociality, degree centrality, and closeness centrality—were sociometrically assessed in schools. Memory performance in adulthood was measured using the Rey Auditory Verbal Learning Test. The sibling fixed effects estimates indicate that sociality, degree centrality, and closeness centrality are significantly associated with increased memory performance in adulthood, even after controlling for unobserved family heterogeneity as well as a set of individual-level covariates. In contrast, controlling for unobserved family heterogeneity attenuated the association for popularity, making it statistically insignificant. This study also provides evidence of gender differences in the association between social network position and memory performance. The associations for popularity, sociality, and degree centrality are more pronounced among men than women. This study’s findings highlight the importance of adolescent network positions as social determinants in shaping cognitive outcomes over the life course. Interventions that encourage positive peer interactions and reduce social isolation during adolescence may help improve cognitive health in the population.     

The mental health recovery movement and family therapy, part II: a collaborative, appreciative approach for supporting mental health recovery

A continuation of Part I, which introduced mental health recovery concepts to family therapists, Part II of this article outlines a collaborative, appreciative approach for working in recovery-oriented contexts. This approach draws primarily upon postmodern therapies, which have numerous social justice and strength-based practices that are easily adapted in recovery-oriented contexts. The model outlined in this article includes an overview of the recovery partnership (i.e., therapeutic relationship), mapping recovery (i.e., assessment and case conceptualization), recovery planning (i.e., treatment planning), facilitating recovery (i.e., intervention), accessing resources (i.e., case management), recovery maintenance, and service contexts as well as a case study.     

Beyond and below racial homophily: ERG models of a friendship network documented on Facebook

A notable feature of U.S. social networks is their high degree of racial homogeneity, which is often attributed to racial homophily--the preference for associating with individuals of the same racial background. The authors unpack racial homogeneity using a theoretical framework that distinguishes between various tie formation mechanisms and their effects on the racial composition of networks, exponential random graph modeling that can disentangle these mechanisms empirically, and a rich new data set based on the Facebook pages of a cohort of college students. They first show that racial homogeneity results not only from racial homophily proper but also from homophily among coethnics of the same racial background and from balancing mechanisms such as the tendency to reciprocate friendships or to befriend the friends of friends, which both amplify the homogeneity effects of homophily. Then, they put the importance of racial homophily further into perspective by comparing its effects to those of other mechanisms of tie formation. Balancing, propinquity based on coresidence, and homophily regarding nonracial categories (e.g., students from "elite" backgrounds or those from particular states) all influence the tie formation process more than does racial homophily.