Volume 32, Issue 1, 2012
|Issue 1, 1-52|
How International Are International Congresses?, 1-11|
Christian Stegbauer & Alexander Rausch
Our study pursues two goals: to present a new method for the analysis of weighted bimodal networks and to show that world
congresses lead to fewer international contacts among the contributors than is generally assumed. The study shows that this
tendency to endogamy can be observed in the contributors of international congresses. For this purpose two world congresses
in the field of sociology are analyzed: the world congress of the IIS in Stockholm (2005) and that of the ISA in Durban (2006).
Proceeding from data about the home countries of the contributors in the diverse sessions, a weighted, bimodal network is developed
by entering the number of contributors from all the different countries of origin for each session. An analysis of this
network represents the focal point of this study. In this context the maximum number of (possible) relationships of attendees
from one and the same country is of special interest. These quantities are subjected to a statistical analysis by comparing them
to analogously calculated quantities obtained from 1000 randomly drawn bimodal networks with the same marginals as those
under discussion. It is found that the homogeneity of the geographical origin of the contributors within the sessions of an international
congress is much greater than would be expected by pure coincidence. This holds true even without taking into account
the fact that co-authors often come from the same country.
Network Topography, Key Players and Terrorist Network, 12-19|
Sean F Everton
In recent years social network analysis (SNA) has enhanced our understanding of how terrorist networks organize themselves and has offered potential strategies for their disruption. To date, however, SNA research of terrorist networks has tended to focus on key actors within the network who score high in terms of centrality or whose structural location (i.e., their location within the overall network) allows them to broker information and/or resources within the network. However, while such a focus is intuitively appealing and can provide short-term satisfaction, it may be putting the cart before the horse. Before jumping to the identification of key actors, we need to first explore a network’s overall topography. Research suggests that networks that are too provincial (i.e., dense, high levels of clustering, an overabundance of strong ties) too cosmopolitan (i.e., sparse, low levels of clustering, an overabundance of weak ties), too hierarchical (i.e., centralized, low levels of variance) and/or too heterarchical (i.e., decentralized, high levels of variance) tend not to perform as well as networks that maintain a balance between these extremes. If these dynamics hold true for terrorist networks as well, then the key player approach may be appropriate in some circumstances, but may lead to deleterious results in others. More importantly, it suggests that analysts need to consider a network’s overall topography before crafting strategies for their disruption.
Poverty and Sociability in Brazilian Metropolises: Comparing poor people’s personal networks in São Paulo and Salvador, 20-32|
Renata Mirandola Bichir & Eduardo Marques
Urban poverty encompasses multiple dimensions including distinctive patterns of sociability, as we have recently learned from
research carried out in the cities of São Paulo and Salvador, Brazil. Starting with preliminary studies focusing on the role personal
networks play in the reproduction of urban poverty, this article aims to compare the personal networks of poor people in these two
important Brazilian metropolises, focusing on different types of personal network. Preliminary findings reveal a wide variety of
of network types, both in São Paulo and Salvador, but also show great similarity between the two cities. Results show that poor
people’s networks are quite diverse, although in general they are smaller and less diversified in their sociability profiles than middle-
class networks. We also confirmed the relevance of the structure of poor people’s networks – and their sociability profiles – in
explaining social conditions, looking at inclusion in the labor market, income generation and other dimensions (Marques, 2010a).
Assessing A Novel Approach To Identifying Optimal Threshold Levels For Cognitive Consensus Structures: Implications and general applications, 33-36|
Previous research has demonstrated the importance of cognitive social network structures to better understanding human behavior
and thought. Yet network members may deviate in perceiving whether relations exist between pairs of nodes in a network,
which can present a challenge in modeling cognitive consensus structures. It has been suggested to define cognitive consensus
structures (CCS) to yield a minimum threshold level of 50% of network members perceiving that a relation exits. Here I suggest
an improved operational definition, labeled optimal cognitive consensus structures (OCCS). The OCCS threshold level is a
function of a consensus structure; yielding the maximum correlate with the summation of nodes’ cognitive interpretations of a
social network. Revisiting two datasets, I find that the OCCS’ predictive validity outperforms the CCS concept in most cases. I
also argue how the OCCS can be further developed as a general tool for optimally dichotomizing valued relational data.
An Introduction to Personal Network Analysis and Tie Churn Statistics Using E-NET, 37-48|
Daniel S. Halgin & Stephen P. Borgatti
In this article we review foundational aspects of personal network analysis (also called ego network analysis) and introduce E-NET (Borgatti 2006), a computer program designed specifically for personal network analysis. We present the basic steps for personal network data collection and use E-NET to review key measures of personal network analysis such as size, composition and structure. We close by introducing longitudinal measures of personal network change, including tie churn, brokerage elasticity, and triad change. We argue that these measures can help reveal change patterns consistent with tie formation strategies that would otherwise be missed using more traditional analytic approaches.
Data Exchange Network, 49-52|
Camp 92 Dataset