Journal of Social Structure

Online ISSN: 1529-1227
The structural cohesion model is a powerful theoretical conception of cohesion in social groups, but its diffusion in empirical literature has been hampered by operationalization and computational problems. In this paper we start from the classic definition of structural cohesion as the minimum number of actors who need to be removed in a network in order to disconnect it, and extend it by using average node connectivity as a finer grained measure of cohesion. We present useful heuristics for computing structural cohesion that allow a speed-up of one order of magnitude over the algorithms currently available. We analyze three large collaboration networks (co-maintenance of Debian packages, co-authorship in Nuclear Theory and High-Energy Theory) and show how our approach can help researchers measure structural cohesion in relatively large networks. We also introduce a novel graphical representation of the structural cohesion analysis to quickly spot differences across networks.
This paper is about estimating the parameters of the exponential random graph model, also known as the p # model, using frequentist Markov chain Monte Carlo (MCMC) methods. The exponential random graph model is simulated using Gibbs or Metropolis-Hastings sampling. The estimation procedures considered are based on the Robbins-Monro algorithm for approximating a solution to the likelihood equation.
ABSTRACT: This chapter discusses theoretical sociology in historical perspective: From the classic tradition to post-classical efforts of synthesis that culminated in multiple paradigms, to the situation today in which theorists are more and more constructing formal models as essential components of their methodology. The classical phase is treated very briefly and the discussion of the post-classical phase is limited to two major theorists, Parsons and Homans, in terms of their common focus on the Durkheimian problem of social integration. The bulk of the chapter deals with developments in recent theoretical sociology. I describe models of structure and of process before defining two types of models that combine a structural focus with process analysis. Finally, I set out a general perspective on theoretical model building and conclude with a discussion of standards in the assessment of such work.
Residuals Model 1 The node symbols allow for the reading of the extent to which the national imports (top) and exports (bottom) are misrepresented with a given model. Use the following links to access two SVG versions of this graphic to explore the image interactively by the accuracy of flow estimates m1quality.html and by the origin and destination of trade m1country.html .
Residuals Model 5 Use the following links to access two SVG versions of this graphic to explore the image interactively by the accuracy of the flow estimates m5quality.html and by the origin and destination of trade m5country.html .
This paper contributes to an ongoing debate in International Political Economy about the appropriateness of globalization, regionalization and macroeconomic imbalance theory by identifying quantitative estimates for all three tendencies from world trade data. This is achieved with a series of gravity models enhanced stepwise by the mapping of the estimation errors of a given model on representations of the overall structure of trade. This not only allows the identification of imperfections in a given model but also permits the further improvement of the models since any systematic regional organization in the error-terms can be identified. The results of the most elaborated model indicate that single factor explanations of global economic integration are presumably misleading. Instead, each of three explanations captures only part of the ongoing changes, as they can be identified under a comparative static perspective from world trade data.
Given the increasing threat of terrorism and spread of terrorist organizations, it is of vital importance to understand the properties of such organizations and to devise successful strategies for destabilizing them or decreasing their efficiency. However, intelligence information on these organizations is often incomplete, inaccurate or simply not available. This makes the study of terrorist networks and the evaluation of destabilization strategies difficult. In this paper, we propose a computational methodology for realistically simulating terrorist networks and evaluating alternative destabilization strategies. We proceed to use this methodology to evaluate and conduct a sensitivity analysis of the impact of various destabilization strategies under varying information surveillance regimes. We find that destabilization strategies that focus on the isolation of individuals who are highly central are ineffective in the long run as the network will heal itself as individuals who are nearly structurally equivalent to the isolated individuals "move in" and fill the communication gaps.
Summary of ARCs for Study 1
The order in which people freely recall a set of words, persons' names, or other items indicates how they organize those items in memory. An individual's cognitive structure of the persons with whom he or she has some particular relation can be described by noticing how he or she associates from one person to the next during recall. Using improved statistical measurement, we review in detail five studies that systematically examined associative patterns in the recall of persons and evaluate competing hypotheses about the nature of these patterns. Individuals in these studies recalled their acquaintances, coworkers, and friends. Across studies, the results consistently show that persons recalled adjacently or successively are perceived to interact more with each other than those not recalled adjacently. No other factor describes associative patterns as well as this notion of perceived social proximity. These results, along with related research, imply the influence of social networks on memory for persons and suggest a universal feature of human social cognition.
Data structures comprising many binary variables can be represented graphically in various ways. Depending on the purpose different plots might be useful. Here two ways of showing associations between variables and implications between variables are discussed. The methods are based on conditional independence graphs and lattices of maximal cluster-property pairs. Applications to multivariate samples and network data are briefly discussed.
This paper examines the degree to which the constraints imposed by various social contexts influence social interaction. We draw on two data sets. In each, we compare the patterning of interaction of the same individuals across different contexts. If minimal constraints are imposed, then the interaction patterns among the individuals in the two contexts should be similar. But if one of the contexts involves major constraints, then interaction patterns in the two should differ. The results suggest further that the constraints found in any context are not unlimited in their impact. Moreover, individuals who can, apparently do manipulate the context to minimize the constraint imposed by the context.
Abstract ,Most personal (egocentric) network studies describe networks using measures that are not structural, opting instead for attribute-based analyses that summarize the relationships of the respondent to network members. Those researchers that have used structural measures have done so on networks of less than 10 members who represent the network core. Although much,has been learned by focusing on attribute-based analyses of personal network data, the application of structural analyses that are traditionally used on whole (sociocentric) network,data may,prove fruitful. The,utility of this approach becomes,apparent when the sample of network members,elicited is relatively large. ,Forty six respondents freelisted 60 network members,and evaluated tie strength between all 1,770 unique pairs of members. Graph -based measures of cohesion and subgroups,revealed variability in the personal network,structure. Non - hierarchical
Global diffusion of the Framework Convention on Tobacco Control
General statistics of the GLOBALink referral network
GLOBALink referral network  
Degree distribution of nodes, in a log-­-log graph. Exponent of power function is 0.934
Total number of nodes over time  
Network analysis has become a popular tool to examine data from online social networks to politics to ecological systems. As more computing power has become available, new technology-driven methods and tools are being developed that can support larger and richer network data, including dynamic network analysis. This timely merger of abundant data and cutting edge techniques affords researchers the ability to better understand networks over time, accurately show how they evolve, find patterns of growth, or study models such as the diffusion of innovation. We combine traditional methods in social network analysis with new innovative visualizations and methods in dynamic network studies to explore an online tobacco-control community called GLOBALink, using almost twenty years of longitudinal data. We describe the methods used for the study, and perform an exploratory network study that links empirical results to real-world events.
Standardized Warfare Advantage by Structural Position
Net Resource Exploitation by Structural Position
Building on world-systems theory, simulation models of 5-line intersocietal networks were generated in an effort to understand systemic power hierarchies. The societal nodes were exclusively connected by three types of interaction: migration, warfare, and unequal trade. These networks can be considered "mixed relation" networks due to the ways in which these types of ties combine positive and negative sanction flows. Insights from elementary theory were employed to understand how exclusion from these different types of ties might influence the resulting power distributions. Additionally, the resource carrying capacity of the nodes was varied by structural position in an effort to differentiate the influence of structural position and individual attributes on location in the hierarchy. It was determined that exclusion from interaction is likely a structural, scale invariant mechanism that helps to determine power distributions above and beyond the inherent attributes of network actors.
Bipartite graph representing a hypothetical affiliation network.
Hypergraph representing a hypothetical affiliation network.
Degrees, Maximum Mutual Affiliations, and Affiliation Indices
First-Order Affliliation and Two-Mode Centrality for 20 Corporate Directors
Correlation Matrix
An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors' affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.
Homophily Parameters: Preferred Age Model (Collapsing sexes)
2004 Male Social Distance-Age
Homophily Parameters: Preferred Education Model (Collapsing sexes)
2004 Male Social Distance-Education
How has the passage of time impacted the ego networks of males and females? I compare the homophily and social distances of males and females using the 1985 and 2004 GSS networks modules. The results indicate that change has been gradual and incremental rather than radical. In 2004 less social distance separates associates for women than for men, and males differentiate more among levels of education. The results suggest that macro-level structural changes have not been sufficient to produce similarly large changes in ego network composition. © 2015, Japan Society of Histological Documentation. All rights reserved.
Social network in Cluster A*
Percentage of females in each male social network
Basic network structure indicators for each cluster
Family planning programs have made significant contributions to lowering fertility levels in several developing nations. These advances often focus on women as the main agents of population control, ignoring the important role of men. However, in many countries/cultures decisions about fertility are highly embedded in social relationships at all levels, which make it imperative to investigate men’s position in the social structure. This study explores the relationship structures between men in Bangladesh using social network analysis to explore new possibilities for cost-effective healthcare strategies that have more far-reaching effects than the status quo. The results of this research show that men are embedded in un-fragmented and diffuse communication structures, formed across age and educational divide, beyond the bounds of kinship relations and village boundaries. Not only do men not shy away from discussion of contraceptives, but also approve and support their use. Men’s networks, thus, provide a potentially rich, but untapped, channel of communication for effectively and efficiently disseminating population control initiatives.
In the United States, young Black men who have sex with men (YBMSM) remain disproportionately affected by HIV. The social networks in which YBMSM are embedded are generally understood to be critical factors in understanding their vulnerability. In this study, we acknowledge the relational richness of YBMSMs' social environments (what we define as multiplexity) and their increasing prioritization of online social networking sites (SNS). Specifically, we investigate whether protective and/or risky features of YBMSMs' Facebook friendships and group affiliations are related to their HIV prevention and sex behavior engagement, while also accounting for features of their offline confidant (or support) and sex networks. Using data from a population-based cohort study of YBMSM living in Chicago (N=268), we perform a series of multiple logistic regression analyses to examine associations between features of YBMSMs' Facebook, confidant, and sexual networks with three prevention outcomes and three sex behavior outcomes, while also controlling for factors at the individual and structural levels. Results show that network features play a more significant role in predicting engagement in sex behaviors than prevention behaviors. Specifically, having more confidants, having confidants who are family members, meeting sex partners online, having more YBMSM Facebook friends, belonging to Facebook groups with an LGBTQ focus, and having greater subject diversity in one's Facebook group affiliations were significantly associated with one or more sex behavior outcomes. We conclude with a discussion of the implications of our findings for HIV prevention intervention efforts.
Hansell's friendship data: 2-block model
We propose a sender-specific blockmodel for network data which utilizes both the group membership and the identities of the vertices. This is accomplished by introducing the edge probabilities (θi,v) for 1 ≤ i ≤ c, 1 ≤ v ≤ n, where i specifies the group membership of a sending vertex and v specifies the identity of the receiving vertex. In addition, group membership is consider to be random, with parameters (Pi)ic=1·We present methods based on the EM algorithm for the parameter estimations and discuss the recovery of latent group memberships. A companion model, the receiver-specific blockmodel, is also introduced in which the edge probabilities (ψu,j) for 1 ≤ u ≤ n, 1 ≤ j ≤ c depend on the membership of a vertex receiving a directed edge. We apply both models to several sets of social network data. © 2015, Japan Society of Histological Documentation. All rights reserved.
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.
How do civilians select internal displacement destinations during conflict? Existing research emphasizes the value of cascades as a guide to making these difficult decisions. Cascades may involve civilians following people in their social networks (community cascades), people with similar characteristics (co-ethnic cascades), or the crowd in general (herd cascades). Analyses relying upon interview or regression-based methodological approaches face substantial challenges in identifying the prevalence of and relationship between each type of cascade. While interview-based approaches can incorporate location characteristics and movement patterns, they struggle with assessing aggregate trends. Meanwhile, regression-based approaches can assess aggregate trends, but they struggle with incorporating location characteristics and movement patterns. Exponential Random Graph Models (ERGMs) that conceive of locations as nodes in a network and movements between those locations as ties can overcome these challenges and assess aggregate trends while incorporating location characteristics and movement patterns. This paper demonstrates the utility of this approach using data from UNHCR on internal displacement in Somalia from 2007-2013. Results reveal that herd cascades only form at high displacement levels, co-ethnic cascades form at medium and high displacement levels, and community cascades form at all displacement levels. Therefore, cascades provide stronger guides for displacement-related decisions as civilians switch from following the crowd in general to following those with similar characteristics to following social ties.
This article discusses the conceptualization of network in Manuel Castells’ theory of network society and its relation to network analysis. Networks assumed a significant role in Castells’ opus magnum, The Information Age trilogy, in the latter half of the 1990s. He became possibly the most prominent figure globally in adopting network terminology in social theory, but at the same time he made hardly any empirical or methodological contribution to network analysis. This article sheds light on this issue by analyzing how the network logic embraced by Castells defines the social, economic, and political relations in his theory of network society, and how such aspects of his theory relate to social network analysis. It is shown that Castells’ institutional network concept is derived from the increased relevance of networks as the emerging form of social organization, epitomized by the idea of global networks of instrumental exchanges. He did not shed light on the internal dynamics of networks, but was nevertheless able to use network as a powerful metaphor that aptly portrayed his idea of the new social morphology of informational capitalism. © 2015. International Network for Social Network Analysis (INSNA).
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.
Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary’s karate club, a canonical dataset from the network literature. © 2014, International Network for Social Network Analysis. All rights received.
In this paper, we employ archival materials from multiple institutional sources to reconstruct the dynamic network of interorganizational collaboration that emerged in response to the Hurricane Katrina disaster of late 2005. Over the period from initial storm formation through the first week following landfall in Louisiana, we record active participation by over 1,500 organizations in response activities. We here conduct an exploratory analysis of the growth and evolution of the network of collaboration among responding organizations, an identification of organizations that emerged as central actors in the response process, and the cohesive subgroups that crystallized within the larger network. Finally, we conclude with a discussion of several issues related to the use of archival methods in research on interorganizational networks in disaster settings, and to the use of automated methods for network extraction.
Map of Economic Research Collaboration between 1985 and 2011
Most studies concerned with empirical social networks are conducted on the level of individuals. The interaction of scientists is an especially popular research area, with the growing importance of international collaboration as a common sense result. To analyze patterns of cooperation across nations, this paper investigates the structure and evolution of cross-country co-authorships for the field of economics from 1985 to 2011. For a long time economic research has been strongly US centered, while influencing real-world politics all over the globe. We investigate the impact of the general trend of increasing international collaboration on the hegemonic structures in the “global department of economics.” A dynamic map of economic research is derived and reveals communities that are hierarchical and structured along the lines of external social forces, i.e. historical and political dimensions. Based on these findings, we discuss the influence of the core-periphery structure on the production of economic knowledge and the dissemination of new ideas. © 2015, Japan Society of Histological Documentation. All rights reserved.
Top-cited authors
Tom A B Snijders
  • University of Groningen
Marc A Smith
Howard T. Welser
  • Ohio University
Eric Gleave
  • University of Washington Seattle
Daniel A Mcfarland
  • Stanford University