Volume 12 (2011): Issue 1 (January 2011)

Labor mobility, both as a mechanism of knowledge diffusion and as a kind of social glue that holds together small production communities operative within a given territory, deserves serious consideration. In this context, focusing on a specific industrial cluster in Ankara, this paper reveals the extent and characteristics of the social networks created by the mobile laborers in order to understand the interconnections between social context, knowledge spillovers, innovation and labor mobility. For this purpose a step-wise algorithm is employed in order to identify social sub-groups by employing social network analysis and by drawing on the flow data constructed for this study. What is evident from this study is that the social network created by the mobility of laborers in Siteler, an industrial cluster specialized in furniture production, reveals a topography of social relations that cannot be split into equally large blocks but eventually parceled out to micro parts consisting of generally 2 or 3 firms. Interestingly, the contexts of innovation also unveil that innovative firms tend to be located at an intermediate position, not an upper and central position, within the topography of the network.

Our goal in this paper is to explore two generic approaches to disrupting dark networks: kinetic and non-kinetic. The kinetic approach involves aggressive and offensive measures to eliminate or capture network members and their supporters, while the non-kinetic approach involves the use of subtle, non-coercive means for combating dark networks. Two strategies derive from the kinetic approach: Targeting and Capacity-building. Four strategies derive from the non-kinetic approach: Institution-Building, Psychological Operations, Information Operations and Rehabilitation. We use network data from Noordin Top’s South East Asian terror network to illustrate how both kinetic and non-kinetic strategies could be pursued depending on a commander’s intent. Using this strategic framework as a backdrop, we strongly advise the use of SNA metrics in developing alterative counter-terrorism strategies that are contextdependent rather than letting SNA metrics define and drive a particular strategy.

Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation, early warning, and faster response to both positive and negative organizational activities. By applying statistical process control techniques to social networks we can rapidly detect changes in these networks. Herein we describe this methodology and then illustrate it using four data sets, of which the first is the Newcomb fraternity data, the second set of data is collected on a group of mid-career U.S. Army officers in a week long training exercise, the third is the perceived connections among members of al Qaeda based on open source, and the fourth data set is simulated using multi-agent simulation. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.

Bonacich (1987) suggested a family of centrality measures that provide a useful way of modeling questions of power and network constraint. However, the literature offers little guidance regarding the choice of β, the parameter which alters the way the measure accounts for the effect of having powerful contacts in ones network. In this paper I explore the way the choice of the β parameter affects the power indices the Bonacich measure generates. I consider three network properties which might affect the way the choice of β influences the Bonacich power indices. I find that in high density networks with few internal ‘chains’ and few pendants, the choice of β is largely immaterial. Conversely, in sparse networks, those with a high proportion of pendant nodes, or those with many chains, the value of β has a substantial effect on the power indices the measure generates. Next I consider whether power indices produced by interior values of β might be represented as a linear combination of “pure” vectors, those generated with values of β at either end of the parameter range and β = 0. I find that in the vast majority of cases a linear combination of “pure” vectors power is equivalent to using indices produced by interior values of β, making the choice of β largely moot. Finally, in the unlikely case that this disaggregation is inappropriate, I discuss the question of determining an appropriate value of β empirically.

In the present study, the social linkages of street-involved youth and correlates of infection with chlamydia and gonorrhea are explored. This is the first study to assess the social linkages of street- involved youth using RDS. Eleven street-involved youth aged 14 to 24 were selected as seeds to recruit their peers into the study using RDS (N=169). Study staff administered a questionnaire, obtained a urine specimen, and provided recruitment coupons to participants. A week later, participants were provided with test results and treatment if necessary. RDS Analysis Tool was used to assess the effectiveness of RDS and define the social linkages. A Fisher’s Exact test was used to identify any correlates of infection. Gender was the only variable that correlated with infection status (22 percent of females vs. 8 percent of males). A high proportion of male participants had never been tested before. Despite the fact that most female participants had been tested before, high infection rates indicate that more accessible and frequent testing is required. Street-involved youth are connected socially to those who share similar health related behaviors. There is a need for increased testing options and opportunities for street-involved youth.

This paper introduces a new computer-based visualization method, the parallel arc diagram (PAD), which is capable of uniquely representing 2-mode temporal relationships in a manner that assists in highlighting simple features of the network. The PAD approach relies on a computer’s ability to render link lines adjacent to each other with orderly precision, resulting in features that facilitate preattentive processing of simple network characteristics and providing the ability to discern patterns of interactions over time. PADs supplement existing methods such as node-link diagrams by offering a simple alternative visualization without the computational complexity of graph layout algorithms and the additional issues that animation introduces. This paper subjectively evaluates the PAD approach using low level task taxonomies developed for assessing adjacency matrix and node-link visualization effectiveness. We argue based on those taxonomies that the PAD approach is as effective or in some cases more effective than existing approaches except for tasks requiring the identification of structural groups or middle-man nodes. This paper also demonstrates how the PAD approach can be utilized in a software application. The TIPAD (Temporal Interactive Parallel Arc Diagram) uses character participation in movie scenes as a test-bed for exploring social interactions over time and provides the ability to compare a PAD based visualization with traditional visualizations of the same network.

We propose a visual representation of bibliographic data based on shared references. Our method employs a distance metric that is derived from bibliographic coupling and then subjected to fast approximate multidimensional scaling. Its utility is demonstrated by an explorative analysis of social network publications that, most notably, depicts the genesis of an area now commonly referred to as network science. However, the example also illustrates some common pitfalls in bibliometric analysis.