Internal cohesion of ls sets in graphs

Let G be a finite connected graph. A set of vertices H ? V(G) is called a LS set if for every proper subset K ? H, there are more edges linking K to H ? K than there are linking K to V(G)-H. Since “cliques” in social networks have usually been seen informally as sets of individuals more closely tied to each other than to outsiders, LS sets provide a natural realization of the “clique” concept. In this paper, it is shown that LS sets in social networks have cohesive properties that make them even more useful for empirical analyses. In particular, subgraphs induced by LS subsets remain connected even after several edges have been removed. Results bounding the number of edges that can be so removed are used to get an upper bound for the diameter of subgraphs induced by LS subsets.     

Detecting large cohesive subgroups with high clustering coefficients in social networks

Clique relaxations are used in classical models of cohesive subgroups in social network analysis. Clustering coefficient was introduced more recently as a structural feature characterizing small-world networks. Noting that cohesive subgroups tend to have high clustering coefficients, this paper introduces a new clique relaxation, α-cluster, defined by enforcing a lower bound α on the clustering coefficient in the corresponding induced subgraph. Two variations of the clustering coefficient are considered, namely, the local and global clustering coefficient. Certain structural properties of α-clusters are analyzed and mathematical optimization models for determining α-clusters of the largest size in a network are developed and validated using several real-life social networks. In addition, a network clustering algorithm based on local α-clusters is proposed and successfully tested.     

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.     

Social network structure and the trade-off between social utility and economic performance

We put forward a computational multi-agent model capturing the impact of social network structure on individuals’ social trust, willingness to cooperate, social utility and economic performance. Social network structure is modeled as four distinct social capital dimensions: degree, centrality, bridging and bonding social capital. Model setup draws from socio-economic theory and empirical findings based on our novel survey dataset. Results include aggregate-level comparative statics and individual-level correlations. We find, inter alia, that societies that either are better connected, exhibit a lower frequency of local cliques, or have a smaller share of family-based cliques, record relatively better aggregate economic performance. As long as family ties are sufficiently valuable, there is a trade-off between aggregate social utility and economic performance, and small world networks are then socially optimal. We also find that in dense networks and trustful societies, there is a trade-off between individual social utility and economic performance; otherwise both outcomes are positively correlated in the cross section.     

Information communities: The network structure of communication

This study puts forward a variable clique overlap model for identifying information communities, or potentially overlapping subgroups of network actors among whom reinforced independent links ensure efficient communication. We posit that the average intensity of communication between related individuals in information communities is greater than in other areas of the network. Empirical tests show that the variable clique overlap model is indeed more effective in identifying groups of individuals that have strong internal relationships in communication networks relative to prior cohesive subgroup models; the pathways generated by such an arrangement of connections are particularly robust against disruptions of information transmission. Our findings extend the scope of network closure effects proposed by other researchers working with communication networks using social network methods and approaches, a tradition which emphasizes ties between organizations, groups, individuals, and the external environment.     

The cliquishness of world cities

Abstract Through an exploratory analysis of the world city network, we confront the dearth of studies on inter‐urban networks on a global scale. In the first section, we outline how a formal theoretical specification of the world city network as a social network has guided an extensive data gathering effort. Through this specification it becomes possible to apply standard techniques of network analysis to world cities. In the second section we outline a framework within which to explore the complex patterns that arise from the formal specification of the world city network. The conjectural use of connectivity, world city cliques and clique‐by‐clique comembership is then used in the third section as the input to an exploratory analysis of the measures of connectivity at different thresholds. The main findings are the two city dominance of the New York–London dyad, and the observation that inter‐city links at the global level retain an important regional dimension. Exemplary cases are the thorough interrelatedness of North American, Pacific Asian and European world cities.     

Governing drug reimbursement policy in Poland: The role of the state,civil society,and the private sector

This article investigates the distribution of power in Poland’s drug reimbursement policy in the early 2000s. We examine competing theoretical expectations suggested by neopluralism, historical institutionalism, corporate domination, and clique theory of the post-communist state, using data from a purposive sample of 109 semi-structured interviews and documentary sources. We have four concrete findings. First, we uncovered rapid growth in budgetary spending on expensive drugs for narrow groups of patients. Second, to achieve these favorable policy outcomes drug companies employed two prevalent methods of lobbying: informal persuasion of key members of local cliques and endorsements expressed by patient organizations acting as seemingly independent “third parties.” Third, medical experts were co-opted by multinational drug companies because they relied on these firms for scientific and financial resources that were crucial for their professional success. Finally, there was one-way social mobility from the state to the pharmaceutical sector, not the “revolving door” pattern familiar from advanced capitalist countries, with deleterious consequences for state capacity. Overall, the data best supported a combination of corporate domination and clique theory: drug reimbursement in Poland was dominated by Western multinationals in collaboration with domestically based cliques.     

Informant accuracy in social network data IV: a comparison of clique-level structure in behavioral and cognitive network data

This paper examines whether clique-structure in cognitive data (i.e. recall of who one talks to) may be used as a proxy for clique-structure in behavioral data (i.e. who one actually talks to). The answer to this question is crucial to much of sociometric and social net-theoretic studies of social structure.We analysed the clique structures of the communication patterns of four naturally occurring groups of sizes 34 to 58, whose actual communications could easily be monitored, together with the groups' perceptions of their communications. The groups used were: radio hams, a college fraternity, a group of office workers, and an academic department. The analysis used clique-finding, block-modelling, and factor-analytic techniques, all employed in such a way as to maximize the accuracy of the cognitive data.After defining a way to compare clique structures between behavioral and cognitive data, we found that there was no useful relationship between the two, and furthermore there was no significant difference in performance between any of the structure-finding algorithms.We conclude that cognitive data may not be used for drawing any conclusions about behavioral social structure.     

Perspective-taking and memory capacity predict social network size

Human social networks typically consist of a hierarchically organized series of grouping levels. There is, however, considerable variation between individuals in the sizes of any given network layer. We test between two possible factors (memory capacity and theory of mind) that might limit the size of two different levels within human social networks (support cliques and sympathy groups). We show that the size of an individual's support clique (the number of individual's in the innermost circle of friends) is better explained by individual differences in social cognition (mentalising skills). However, the size of the sympathy group (the most frequent social partners) is better explained by individual's performance on memory tasks.     

The effect of density on the level of bias in the network autocorrelation model

Researchers interested in the effects of social network ties on behavior are increasingly turning to the network autocorrelation model, which allows for the simultaneous computation of individual-level and network-level effects. Earlier research, however, had pointed to the possibility that the maximum likelihood estimates used to compute the network autocorrelation model yielded negatively biased parameter estimates. In this paper we use simulations to examine whether – and the conditions under which – a negative bias exists. We show that the network parameter estimate ρ is negatively biased under nearly all conditions, and that this bias becomes more severe at higher levels of both ρ and network density. We conclude by discussing the implications of these findings for researchers planning to use the network autocorrelation model.     

The structure of educational research: The role of multivocality in promoting cohesion in an article interlock network

Researchers interested in the social and intellectual features of scholarly knowledge have emphasized the importance of small-scale, informally organized groups of people interested in the same or closely related research problems. The structure of these social networks, in large part, influences the way which research problems are identified, studied and transformed into policy. Building on popular concerns about the state of the educational research field, this study examines what its structure implies about the field's ability to integrate ideas and practices. Using data about which articles readers access from the online database of the Teachers College Record, one of the field's more popular journals, it is shown that the article interlock network exhibits a structure that fits parameters of both small-world and structurally cohesive models. Furthermore, this study unpacks the network's community structure to show that those articles that serve as the core through which much of the network is connected possess a multivocal identity; an ambiguous identity that appeals to multiple audiences simultaneously. Results are suggestive of mechanisms that could be used to promote greater cohesion across the network's distinct communities.     

Spine segments in small world networks

Investigations of small world contact networks, defined as networks with a short characteristic path length and a substantial local clustering of contacts in the neighborhood of each node, have emphasized the process performance of such networks. The argument that large-scale, small world, contact networks are structures with startlingly efficient process performance is premised on the existence of shortcuts, without which the characteristic path lengths of the networks would be substantially larger. No doubt, given a high probability of transmission in each contact of a network, such shortcuts are a potential structural basis of reliable flows of information, influence, material and disease. However, interpersonal contacts are often markedly unreliable transmission conduits, and the average shortcut contact may be a more unreliable, episodic, transmission conduit than the average contact of cliques. With markedly unreliable contacts, fundamental helix substructures, that are parallel-transmission subsystems of the contact network, importantly enter into the analysis of network performance. These substructures of disjoint path redundancies are based on the local clustering of contacts in the neighborhoods of each node. Drawing on network reliability theory, this article presents an approach in which intersecting cliques of contact networks are a theoretically important construct in the specification of the transmission implications of observed contact networks. Clique intersections are a structural basis of path redundancies that enable reliable transmission among the nodes of contact networks consisting of contacts that may or may not be active conduits of transmissions during some period of time. The strong contacts that occur among clique members further enhance the contributions of these path redundancies.     

The persistence of cliques in the post‐communist state. The case of deniability in drug reimbursement policy in Poland

This article explores a key question in political sociology: Can post‐communist policy‐making be described with classical theories of the Western state or do we need a theory of the specificity of the post‐communist state? In so doing, we consider Janine Wedel's clique theory, concerned with informal social actors and processes in post‐communist transition. We conducted a case study of drug reimbursement policy in Poland, using 109 stakeholder interviews, official documents and media coverage. Drawing on ‘sensitizing concepts’ from Wedel's theory, especially the notion of ‘deniability’, we developed an explanation of why Poland's reimbursement policy combined suboptimal outcomes, procedural irregularities with limited accountability of key stakeholders. We argue that deniability was created through four main mechanisms: (1) blurred boundaries between different types of state authority allowing for the dispersion of blame for controversial policy decisions; (2) bridging different sectors by ‘institutional nomads’, who often escaped existing conflicts of interest regulations; (3) institutional nomads’ ‘flexible’ methods of influence premised on managing roles and representations; and (4) coordination of resources and influence by elite cliques monopolizing exclusive policy expertise. Overall, the greatest power over drug reimbursement was often associated with lowest accountability. We suggest, therefore, that the clique theory can be generalized from its home domain of explanation in foreign aid and privatizations to more technologically advanced policies in Poland and other post‐communist countries. This conclusion is not identical, however, with arguing the uniqueness of the post‐communist state. Rather, we show potential for using Wedel's account to analyse policy‐making in Western democracies and indicate scope for its possible integration with the classical theories of the state.     

Goal specific social capital and job satisfaction: Effects of different types of networks on instrumental and social aspects of work

This paper addresses the question “To what extent can job satisfaction be explained as the revenue of social capital?” By conceiving someone’s social network as social capital we specify conditions under which social ties do lead to job satisfaction. We inquire into the idea of goal specificity of social capital, which implies that a network with a given structure and content will have different impacts on various aspects of job satisfaction. If the content of the ties and the structure of the network at the job engender material well-being or produce social approval, satisfaction with the corresponding job aspects increases. Data were collected in 1993 using written questionnaires in two Dutch governmental agencies, one with 32 and the other with 44 employees. These workers’ networks were charted using nine name-generating questions.Social capital, it turns out, is not an all-purpose good but one that is goal specific, even within a single domain of life such as work. Three effects stand out: First, the structure of the network and the content of the ties do matter. Networks of strategic, work-related ties promote an employee’s satisfaction with instrumental aspects of the job, like income, security, and career opportunities. Second, closed networks of identity-based solidarity ties improve an employee’s satisfaction with social aspects of the job, like the general social climate at work and cooperation with management and colleagues. Third, a network with a bow–tie structure (i.e., where a focal actor is the link between two or more mutually exclusive cliques) generally has strong negative effects on satisfaction with the social side of the job; although a bow–tie type network of trusting ties does increase satisfaction with the social side. This implies that Krackhardt’s hypothesis on the unpleasant feelings produced by bow–tie type networks has to be specified for the content of the ties that constitute such a network. The most important conclusion of our analysis is that goal specificity of social capital has implications for both structure and content of social networks. Achievement of a particular goal, such as satisfaction at work, requires not only networks of a certain structure or ties with a particular content, but specifically structured networks of ties with a particular content.     

How Do Efforts to Enhance Career Preparation Affect Peer Groups?

The present study investigated how efforts to enhance career preparation affect peer groups. The participants were 710 9th graders who were randomized into control and intervention groups and assessed 3 times during 1 academic year: at baseline (T1, Fall), immediately after the career preparation intervention (T2, 3 months after baseline), and 5 months after the intervention (T3, Spring). The results showed, first, that maintenance of a stable clique was more likely when most clique members participated in the intervention. Second, it was found that the members of adolescents' cliques resembled each other in respect of the strength of the intervention effect. Finally, the results showed that the similarity of the clique members in career choice preparedness first decreased as a result of the short‐term impact of the intervention but then increased after the intervention was over, suggesting that peer groups were partly responsible for weakening the effect of the intervention. These results have important implications for understanding how peer groups moderate external influences on their members and how the stability of peer groups is affected by these influences.     

The Network Autocorrelation Model using Two-mode Data: Affiliation Exposure and Potential Bias in the Autocorrelation Parameter

The network autocorrelation model has been a workhorse for modeling network influences on individual behavior. The standard network approaches to mapping social influence using network measures, however, are limited to specifying an influence weight matrix (W) based on a single mode network. Additionally, it has been demonstrated that the estimate of the autocorrelation parameter ρ of the network effect tends to be negatively biased as the density in W matrix increases. The current study introduces a two-mode version of the network autocorrelation model. We then conduct simulations to examine conditions under which bias might exist. We show that the estimate for the affiliation autocorrelation parameter (ρ) tends to be negatively biased as density increases, as in the one-mode case. Inclusion of the diagonal of W, the count of the number of events participated in, as one of the variables in the regression model helps to attenuate such bias, however. We discuss the implications of these results.