Volume 14 (2013): Issue 1 (January 2013)

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.

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.

Often objects are to be ranked. However, there is no measurable quantity available to express the ranking aim and to quantify it. The consequence is that indicators are selected, serving as proxies for the ranking aim. Although this set of indicators is of great importance for its own right, the most commonly used practice to obtain a ranking is an aggregation method. Any aggregation, however suffers from the effect of compensation, because the aggregation technique is in the broadest sense an averaging method. Here an alternative is suggested which avoids this averaging and which is derived from simple elements of the theory of partially ordered sets (posets). The central concept in partial order is the ‘concept of comparison’ and the most general outcome is a web of relations between objects according to their indicator values, respecting the ranking aim.

As an example the ‘Failed State Index’ (FSI), annually prepared by the Fund of Peace is selected. The FSI is based on twelve individual contextual different indicators, subsequently transformed into a single composite indicator, by simple addition of the single indicator values. Such an operation leaves space for compensation effects, where one or more indicators level out the effect of others. Hence, a comparison between the single states (in total 177) based on their mutual FSI ranking has its limitations as the comparisons are made based on the composite indicator. We show that brain drain is one of the indicators in the FSI-study that plays a crucial role in the ranking, whereby the ranking aim is the stabilization of nations.

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.