Understanding how students form and maintain social networks in volunteer organizations is crucial for promoting collaboration, effective communication, and the flow of resources within these groups. While prior research has often relied on self-reported networks, few studies have examined the discrepancies between perceived and actual social ties, particularly within student volunteer associations in Romania. The present study analyzed personal friendship and help networks in the human resources department of a university-based student volunteer association. Central to the investigation was self-monitoring, a variable introduced by Snyder (1974), examined in relation to cognitive social networks, network centrality, physical proximity, and dependency relationships. Statistical and network analyses revealed that students with higher self-monitoring scores demonstrated greater precision in identifying relationships, occupied more central positions within both friendship and help networks, maintained more connections, and strategically managed dependency by offering help more often than requesting it. Additionally, students residing on the association’s campus were considered close friends and received more help requests than those living elsewhere, highlighting the role of physical proximity in shaping relational patterns and network popularity. These findings provide insights into how individual traits and contextual factors jointly shape social network formation in student volunteer organizations and extend understanding of cognitive and structural dynamics in these settings.
Centrality measures are widely utilized in complex networks to assess the importance of nodes. The choice of measure depends on the network type, leading to diverse node rankings. This paper aims to compare various centrality measures by examining their correlations. We specifically focus on the Pearson correlation coefficient and Spearman correlation. Pearson correlation considers node centrality values, while Spearman correlation is based on node ranks. Our study encompasses different network topologies, including random, scale-free, and small-world networks. We investigate how these network structures influence correlation values. The main part of the paper describes the relationship between correlations and network model parameters. Additionally, we explore the impact of global network characteristics on correlations, as well as their direct connection to network parameters. Through a systematic review of literature-based centrality measures, we have identified and selected the most commonly employed ones to investigate their correlation including degree centrality, betweenness centrality, eigenvector centrality, and closeness centrality. Our findings reveal that correlations in random networks are minimally affected by network structure, whereas restructuring significantly impacts correlations in other networks. In particular, we show a notable impact of structural parameter variations on correlations within small-world networks. Furthermore, we demonstrate the substantial influence of fundamental network characteristics such as spectral gap, global efficiency, and majorization gap on correlations. We show that amongst the various properties, the spectral gap stands out as the most valuable indicator for estimating correlations.
This volume was hard for me to evaluate.My conception of a handbook, on any topic, is that it needs to be comprehensive in its coverage.While I understand complete coverage of any field is exceedingly difficult, if not impossible, this handbook fails this criterion for reasons I will detail below.Even so, there is a large amount of useful information in this handbook despite its serious flaws.While assembling some great chapters, the editors did not draw attention to the many links between the chapters they had assembled.In terms of content, there are thirty-three chapters on a diverse set of social network analytic content.Due to space limitations, I cannot comment on them all.Some of the chapters are most informative and useful.While the opening chapter ("Introduction"), written by the editors, points towards divisions in the field, the chapter does little to deal with them.Of course, divisions in the field will exist.Fortunately, other chapters address this issue.Chapter 2 ("Network Basics," Light and Moody) introduces basic concepts in a reasonable fashion.But in doing so, the authors introduce the concept of boundary specification for networks, a most important concept.Yet they do not cite the authors who introduced this critical concern to the literature!Fortunately, this lapse was corrected in Chapters 7 and 8. Again, Chapter 2 provides little linkage between the content of the chapters in this handbook.I find this most unfortunate.Chapter 3 ("Theories of Social Networks," Fuhse) discusses the topic of theories in social networks.Clearly this is an important topic meriting extensive consideration.But the author attacks a manuscript in the literature that made a distinction between "network theory" and the "theory of networks."The distinction is very reasonable.But the author claims there is no theory here but only network mechanisms.Network mechanisms about networks are inherently theoretical.
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Abstract Multi-location knowledge-intensive firms span their value chains and thus their locations across space. Increased globalization alters the spatial configuration of such networks of knowledge creation. Longitudinal social network analysis allows detecting temporal changes in the arrangement of nodes and edges in the network and resulting changes in the overall structure. We use this approach to study for Germany the spatio-temporal dynamics of knowledge-intensive services firms – advanced producer services (APS) – in the years between 2009 and 2019. Multi-location APS firms are considered as vanguard of spatial structural change and thus lending to study their location choice behavior. A common approach is to analyze a one-mode intercity network where cities are the nodes. We take a different approach and include the firms’ perspectives. We work directly with the original data structure of a two-mode network including cities and firms as two node sets and we apply stochastic actor-oriented models for network dynamics. Results show that the spatio-temporal dynamics are characterized by both agglomeration and network economies. On a local scale, APS firms continue their location expansion over time and concentrate in agglomerations where many other APS firms and a greater availability of workforce are present. Simultaneously, they also choose new locations in agglomerations further apart from their present locations. On a supra-local scale, the network grows denser over time. Agglomerations that are attractive for APS firms in 2009 become even more attractive in 2019. Our analysis contributes to an understanding of how interactions amongst cities and firms on a local scale give rise to the empirically observed network patterns on a supra-local scale.
Abstract A recently published paper [Martin (2017) JoSS 18(1):1-21] investigates the structure of an unusual set of social networks, those of the alternate personalities described by a patient undergoing therapy for multiple personality disorder (now known as dissociative identity disorder). The structure of these networks is modeled using the dk-series, a sequence of nested network distributions of increasing complexity. Martin finds that the first of these networks contains a striking feature of a large “hollow ring”; a cycle with no shortcuts, so that the shortest path between any two nodes in the cycle is along the cycle (in more precise graph theory terms, this is a geodesic cycle). However, the subsequent networks have much smaller largest cycles, smaller than those expected by the models. In this work, I re-analyze these delusional social networks using exponential random graph models (ERGMs) and investigate the distribution of the lengths of geodesic cycles. I also conduct similar investigations for some other social networks, both fictional and empirical, and show that the geodesic cycle length distribution is a macro-level structure that can arise naturally from the micro-level processes modeled by the ERGM.
Abstract On some fundamental level, we can think of scholars as actors possessing, or controlling, various types of resources. Collaboration in science is understood here as a process of pooling and exchanging such resources. We show how diversity of resources engaged in scientific collaboration is related to the structure of collaboration networks. We demonstrate that scholars within their personal networks simultaneously (1) diversify resources in collaboration ties surrounded by structural holes and (2) specialize resources in collaboration ties embedded in dense collaboration groups. These complementary mechanisms decrease individual efforts required to maintain effective collaborations in complex social settings. To this end, we develop a concept of “pairwise redundancy” capturing structural redundancy of ego’s neighbors vis-à-vis each other.
BACKGROUND:Despite evidence that obesity and related behaviors are influenced by social networks and social systems, few childhood obesity initiatives have focused on social network factors as moderators of intervention outcomes, or targets for intervention strategies.OBJECTIVES:This pilot study examines associations between maternal social network characteristics hypothesized to influence health behaviors, and the target outcomes of a family-centered childhood obesity prevention initiative. The pilot intervention entailed the provision of healthy eating and activity components as part of an existing home visiting program (HVP) delivered to mothers and infants, to test the feasibility of this approach for improving mother diet, physical activity, and weight status; and infant diet and weight trajectory.METHODS:Mothers and their infants (N=50 dyads) receiving services from our HVP partner were recruited and randomized to receive the HVP core curriculum with or without a nutrition and physical activity enhancement module for six months. Assessments of mothers' social network characteristics, mother/infant food intake and mother physical activity, and mothers' postpartum weight retention and children's growth velocity were conducted at baseline and post-intervention.RESULTS:Several features of mothers' social networks, including the receipt of health-related social support, were significantly associated with the focal intervention outcomes (p < .05) at follow-up, controlling for study condition.CONCLUSIONS:Integrating childhood obesity prevention into HVPs appears promising. Future family-based interventions to prevent childhood obesity may be enhanced by including social network intervention strategies. For example, by addressing family network characteristics that impede healthy behavior change, or enhancing networks by fostering social support for healthy behavior and weight change.
This study investigates how adolescent peer friendship formation relates to help-seeking behavior and how the structure of peer social networks contributes to the creation of social connections by psychological counseling recipients. The study sample comprised 2,264 adolescents ages 12-19 from the National Longitudinal Study of Adolescent Health (Add Health). Stochastic actor-based modeling simulated the co-dependence of peer friendship networks and adolescent help-seeking behavior from an initial data state to a final data state while accounting for social selection and influence effects in the same model. Results indicated that adolescents who sought psychological counseling in the past year nominated 65% more peers as friends than otherwise identical adolescents who did not use psychological services. Adolescent psychological counseling did not contribute to the loss of friends. Users of psychological services were twice as likely to be named as friends in highly interconnected peer social networks (i.e. more friendship connections among their friends), as opposed to individuals in less interconnected peer groups. The findings indicate improved social functioning of adolescents as a result of psychological counseling. The results advocate for use of psychological services and point to the necessity of wide-spread screening and early detection and treatment of mental ill-health among U.S. adolescents. Group interventions targeting building social skills to enhance peer group social network interconnectivity may promote better social connections for adolescent users of psychological counseling.
Background:Family health history is a strong risk factor for many chronic diseases. Ethnic minorities have been found to have a low awareness of their family health history (FHH), which may pose a contributing factor to health disparities. Purpose:The purpose of this mixed-methods social network analysis study was to identify structural and contextual patterns in African American adults' FHH knowledge based on interpersonal communication exchanges with their family members. Methods:African American adults completed individually administered family network interviews. Participants' 3-generation family pedigree served as a visual aid to guide their interview. Our primary outcome of interest for this analysis was whether a family member was reported as someone who talks to the participant about their own (i.e., the family member's) health, which we refer to as a "personal health informant." To contextualize quantitative findings, participants were asked to describe how they learned about the health history of the relatives they identified during their interview. Results:Participants (n=37) reported an average family network size of 29.4 relatives (SD = 15.5; Range = 10-67). Each participant, on average, named 17% of their familial network as personal health informants. Multivariate regression results showed that participants were more likely to name an alter as a personal health informant if the alter was female (OR = 2.14, p = 0.0519), from the maternal side of the participant's family (OR = 1.12, p = 0.0006), had one or more chronic health conditions (OR = 2.41, p = 0.0041), was someone who has discussions with the participant about the participant's health (OR = 16.28, p < 0.0001), was a source of family health information (OR = 3.46, p = 0.0072), and was someone whose health the participant helps to monitor or track (OR = 5.93, p = 0.0002). Complementary qualitative findings indicate that FHH knowledge is facilitated by open, direct communication among relatives. Personal health informants were described as disclosing information for the purposes of informing others for preventive purposes and for gaining social support. Participants also learned about FHH via other methods, including direct observation, during caretaking, and following a relative's death. Conclusions:Communication and disclosure practices is an important determinant of African Americans' FHH knowledge. More culturally and contextually meaningful public health efforts are needed to promote family health history sharing, especially regarding paternal family health history, siblings, and extended relatives.
Abstract To examine predictors of preschool language abilities, thirty-seven infants at high risk for Autism Spectrum Disorder (ASD) were recorded longitudinally from 5-14 months as they interacted with their caregivers and toys at home. Triadic interactions were coded, categorized as transitive, intransitive or vacuously transitive, and then related to the MacArthur Bates Communicative Development Inventory (CDI-III) and the Mullen Scales of Early Learning (MSEL) at 36 months. The results show that prior to 14 months, early transitive interactions correlate positively and intransitive interactions correlate negatively with CDI-III and MSEL scores at 36 months. By categorizing interactions between 5-14 months by transitivity, we have demonstrated that recurring triadic patterns can predict communicative abilities at 36 months.
Abstract Background and objective. Nutrition information conveyed by popular entities through online social networking sites (i.e., social media influencers) has the potential to impact consumer eating behavior through mechanisms of social influence. Little is known about how online communities of food-related social media influencers are structured, which could reveal influencers’ opportunities to observe and spread nutrition-related content and information design practices. This study explored patterns of social relationships (social capital, conservation of resources, and homophily) within a network of prominent food bloggers on Twitter (N = 44). Methods. Data on Twitter following/follower relationships and Twitter use (number of tweets, favorited tweets) were collected from bloggers’ Twitter profiles. Bloggers represented eight topical subcategories of food blogs (e.g., family cooking, cocktails) and comprised a one-mode social network with directed ties indicating Twitter following/follower relationships. Structural evidence of patterns of social relationships was investigated through social network visualization, centrality measures (in-degree/out-degree centrality, density, reciprocity), and inferential tests. Results. The overall network density of directed ties was 21%, with wide variability in individual blogger centrality across multiple measures. Cocktails, cooking, special diets, and culinary travel bloggers had more dense ties to bloggers in their own subcategories. Within the network, favorited tweets and outreach (Twitter following relationships) were positively associated with popularity (Twitter follower relationships). Conclusions. Food bloggers in this study formed a partially connected network, supporting the conservation of resources framework. Homophily was evident in some, but not all, topical subcategories. Associations among Twitter use, outreach, and popularity generally supported the social capital framework. Future studies should explore influencers’ motivations for connecting on social networking sites, and how content and information design practices spread among influencers.
Abstract The successful implementation of technology often hinges on individual beliefs about the innovation being introduced. Little is known about how social networks shape these beliefs. In this study, we examine: (1) whether individual beliefs about technology are influenced by the beliefs of their peers within their social networks (network content); and (2) whether changes in the composition of the social network over time (network churn) moderates the effect of peer beliefs on individual beliefs. We offer and test hypotheses about these relationships using longitudinal social network survey data from hospital staff collected 2 – 4 months before (N = 256) and 3 – 5 months after (N = 284) the implementation of a new electronic medical record (EMR) system at a large, academic hospital. Our findings suggest that peer beliefs about new technology significantly and negatively affect individual beliefs about technology in the early stages of EMR implementation. We also find that the effect of peer beliefs on individual beliefs is stronger in more stable social networks (i.e., social networks that experience few tie deletions over time) and weaker in less stable social networks (i.e., social networks that experience many tie deletions over time). Our study examines social influence in a novel context – the implementation of EMR systems in the hospital setting – and extends network theory by conceptualizing network churn as a moderating variable that may amplify or dampen the effect of networks.
