Volume 30, Issue 1, 2010

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Leadership Insularity: A New Measure of Connectivity Between Central Nodes Networks, 4-10
Arbesman,Samuel, Christakis,Nicholas,A
We combine two foci of interest with respect to community identification and node centrality and create a novel metric termed “leadership insularity.” By determining the most highly connected nodes within each community of a network, we designate the ‘community leaders’ within the graph. In doing this, we have the basis for a novel metric that examines how connected, or disconnected, the leaders are to each other. This measure has a number of appealing measurement properties and provides a new way of understanding how network structure can affect its dynamics, especially information flow. We explore leadership insularity in a variety of networks.
A Measure of Centrality for Dense Networks with Valued Ties, 11-20Barnett,George,A
This paper presents a new measure of centrality, scalar products centrality that is appropriate for dense networks in which link strength is measured with real numbers rather than by a simple dichotomy. Scalar products centrality may be defined by a node’s distance from the center of the set of measured relations that compose a network. Formulas for its calculation based upon the centroid scalar products matrix from classical multidimensional scaling are presented. Two examples are provided and the measure is compared with standard measures of centrality (degree, eigenvector centrality, betweenness and closeness) to demonstrate its validity. As expected, the measure is strongly related to the degree and eigenvector measures and less so with betweenness and closeness.
Network Topology Effects on Correlation between Centrality Measures, 21-28< /br>McCulloh,Ian,
Centrality measures are often used to describe influential nodes in a network. When these measures are highly correlated they may be redundant and when they are uncorrelated they provide unique insight into the network. I propose a network simulation approach that creates networks with varying degrees of Erdos-Renyi randomness and Albert-Barabasi scale-freeness. Using this simulation approach I conduct 10 replications of a full factorial experimental design with varying levels of density and randomness versus scale-freeness. The effects of topology and density on the correlations of degree, betweenness, closeness, and eigenvector centrality are investigated. I find that not only does density and topology affect the correlation of centrality measures, but there exist many interaction effects as well. In general, networks with high Erdos-Renyi randomness tend to have higher levels of correlation between centrality measures than networks with Albert-Barabasi scale-freeness.
Reproductive Health Policy and Interstate Influence, 29-45< /br>Finer,Lawrence,B , Astone,Nan,Marie , Valente,Thomas,W.
This paper compares two models of interstate influence: the proximity model (which posits that states are more influenced by nearby states than farther ones) and the opinion-leader model (which hypothesizes that some states are “regional leaders” which exert a disproportionate influence on all other states). These models are compared for two outcomes, reproductive health policies and general liberalism, using regression models enhanced by network analysis techniques. Data on the 50 states were collected. Dependent variables included policies and spending on reproductive health as well as a broader range of policies designed to measure general liberalism. Independent variables included historical and geographic conditions, socioeconomic factors, political behavior, governmental institutions, and the behavior of elites. Results indicate that a state’s policies on reproductive health (excluding abortion) appear to be a function of both the socio-economics of the state as well as the reproductive health policy of its regional leaders. General liberalism towards reproductive health, in contrast, is largely explained by per capita income and percentage of the state population that is fundamentalist, as well as the liberalism of a state’s geographic neighbors. The importance of acknowledging network effects in state analyses, and of progressive states’ leadership in advancing reproductive health, is underscored.
The Importance of Work-Related Social Ties in Post-Soviet Russia: Co-worker personal support networks in St. Petersburg and Helsinki, 46-56< /br>Lonkila,Markku,
This study considers the extent to which work-related social ties function as a source of social support in Russian workers’ personal networks. The topic is important since, in the case of unemployment or retirement, personal networks are central for the well-being and coping of Russians. In order to illustrate the nature of the Russian case, an explicit comparison between Russian and Finnish workers’ personal networks is carried out. The results are in line with previous findings concerning the workplace as a source of social support in China.
Co-authorship in Italian Workshops on Population Studies: An Analysis with a Network Approach, 57-74< /br>Rivellini,Giulia, , Terzera,Laura,
Interactions, exchanges of ideas and cooperation among scholars are important factors for the advancement of scientific knowledge. Conferences represent one of the most suitable occasions to further scientific interactions, stimulated through the contributions presented either by a single researcher or by a group of authors. Using the books of abstracts from four recent Italian conferences on population studies (Giornate di Studio sulla Popolazione, GSP, 1999, 2001, 2003, and 2005), this research provides an empirical analysis of the collaboration patterns observed among the authors of the papers presented. We followed a social network perspective, in order to find out the determinants of scientific cooperation in the field of Italian demographic studies. The factors playing a major role in determining the actors’ relationships seem to be related to gender and to the proximity of universities or conference seats. Although a high number of participants are represented by isolated nodes, the most common way of collaborating is a dyadic relationship. The larger collaborations are due mostly to the presence of a small number of leading authors that manage a large number of papers. Productivity and the popularity of leading authors are attributed to their senior positions in research groups or their technical and statistical skills. It is difficult to measure such aspects with an analysis approach that is different from network analysis.

Volume 30, Issue 2, 2010

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Structural Redundancy and Multiplicity in Corporate Networks, 4-20< /br>Barnes,Roy, , Burkett,Tracy,
This research presents an intuitive and straight forward method of capturing both structural redundancy and the multiplicity of social ties in a small network of 20 corporate directors across four different social spheres in 1962. Structural redundancy is best thought of as the opposite of a unique tie which emerges in a network of interlocking corporate directors once the affiliations from other non-corporate organizations are included. Unlike the analysis of structural redundancy, the multiplicity of ties recognizes that the number and the configuration of ties between a given pair of directors are both meaningful. By utilizing these concepts, the paper shows how social club ties in 1962 were especially important in adding unique (i.e., non-redundant) ties among the corporate directors. The analysis of multiplicity reveals that over 60 percent of the directors had multiple ties and that 56 percent of the directors possessed ties stemming from two or more different types of social affiliations. These results underscore that there is more to the social cohesion among corporate directors than interlocks alone.
A Configurational Approach to Homophily Using Lattice Visualization, 21-40< /br>Schaefer,David,R
This research approaches homophily as a multidimensional concept and uses combinatorial logic to investigate the patterns of homophily that exist in relations from different substructures. Particular focus is on which patterns of homophily (i.e., configurations) occur more often than expected by chance given the demographic distribution of the population. Lattices are introduced as an intuitive way to represent homophily multi-dimensionally and uncover patterns within and across relation types. Qualitative Comparative Analysis is used as a means to uncover the conjunctural forces that underlie homophily. These techniques are applied to General Social Survey data on discussion relations. Results demonstrate the low, but nonzero, tolerance for dissimilarity in relations and how patterns and levels of homophily vary across relation types. The majority of relations exhibited homophily on only three or four of the five dimensions measured. Race was a key dimension of homophily for all relation types (in fact, necessary for kin and group member relations), while kin relations also required religious homophily and friend relations almost always required age homophily.
Egocentric Networks and Unique and Shared Sources of Social Support, 41-49< /br>Hall,Jeffrey,A
Egocentric social network instruments typically require independently sampled respondents to identify up to five social network alters. When collecting egocentric data from dyads (e.g., mothers and fathers), shared and unique network alters can be identified. The present manuscript describes a new way of using egocentric data collected from related pairs using Multilevel Modeling (MLM). As a case study, the egocentric social support networks of twenty pairs of parents of children with cancer (N = 40) will be analyzed to illustrate how this technique can be used to model the characteristics of each network alter and to answer research questions regarding sex differences in received social support networks.
What You Believe Travels Differently: Information and Infection Dynamics Across Sub-Networks, 50-63< /br>Grim,Patrick, , Reade,Christopher, , Singer,Daniel,J. , Fisher,Steven, , Majewicz,Stephen,
In order to understand the transmission of a disease across a population we will have to understand not only the dynamics of contact infection but the transfer of health-care beliefs and resulting health-care behaviors across that population. This paper is a first step in that direction, focusing on the contrasting role of linkage or isolation between sub-networks in (a) contact infection and (b) belief transfer. Using both analytical tools and agent-based simulations we show that it is the structure of a network that is primary for predicting contact infection—whether the networks or sub-networks at issue are distributed ring networks or total networks (hubs, wheels, small world, random, or scale-free for example). Measured in terms of time to total infection, degree of linkage between subnetworks plays a minor role. The case of belief is importantly different. Using a simplified model of belief reinforcement, and measuring belief transfer in terms of time to community consensus, we show that degree of linkage between sub-networks plays a major role in social communication of beliefs. Here, in contrast to the case of contract infection, network type turns out to be of relatively minor importance. What you believe travels differently. In a final section we show that the pattern of belief transfer exhibits a classic power law regardless of the type of network involved.

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