Interpersonal Status Systems. An Inquiry into Social Networks and Status Dynamics in Schools, Science, and Hollywood

Status systems—vertical orders among persons according to differences in social recognition—are a ubiquitous feature of human societies. Vast streams of research developed to explore how status structures social life. This thesis proposes a unified framework for studying the interplay between social status and social networks. The framework highlights the importance of contextual characteristics for the emergence of status systems in various settings and complements approaches that focus on how individuals gain and perpetuate status. Theoretical expectations derived from this perspective are tested by applying a combination of exponential random graph models and other network-analytical tools to three different empirical settings. The first application investigates whether the structure of friendships and status ascriptions among more than 23,000 adolescents is sensitive to contextual characteristics such as the size or demographic composition of classrooms and grade levels. The second study examines collaboration networks among more than 7,000 neuroblastoma researchers over 40 years. Here, the investigation focuses on changes in the stratification and segregation of collaboration networks as a scientific field grows and matures. Similarly, the third study investigates the interplay between culture, status, and networks among Hollywood filmmakers from 1930 through 2000 by using information on artistic references and collaborations of more than 13,000 filmmakers retrieved from the Internet movie database (IMDb). The results illustrate that the link between status and networks intensifies under certain contextual conditions. One key finding is that larger contexts exhibit networks marked by status recognition in all empirical settings: larger school classes and grade levels produce leading crowds more often than smaller ones, the scientific field of neuroblastoma research developed an elite of researchers as it grew, and social recognition is distributed increasingly unequal during periods in which Hollywood attracted more filmmakers. The thesis closes by comparing the different settings in greater detail and by discussing directions for future research

A Model of Collaboration Network Formation with Heterogenous Skills

Collaboration networks provide a method for examining the highly heterogeneous structure of collaborative communities. However, we still have limited theoretical understanding of how individual heterogeneity relates to network heterogeneity. The model presented here provides a framework linking an individual's skill set to her position in the collaboration network, and the distribution of skills in the population to the structure of the collaboration network as a whole. This model suggests that there is a non-trivial relationship between skills and network position: individuals with a useful combination of skills will have a disproportionate number of links in the network. Indeed, in some cases, an individual's degree is non-monotonic in the number of skills she has--an individual with very few skills may outperform an individual with many. Special cases of the model suggest that the degree distribution of the network will be skewed, even when the distribution of skills is uniform in the population. The degree distribution becomes more skewed as problems become more difficult, leading to a community dominated by a few high-degree superstars. This has striking implications for labor market outcomes in industries where production is largely the result of collaborative effort

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Evolutionary Events in a Mathematical Sciences Research Collaboration Network

This study examines long-term trends and shifting behavior in the collaboration network of mathematics literature, using a subset of data from Mathematical Reviews spanning 1985-2009. Rather than modeling the network cumulatively, this study traces the evolution of the "here and now" using fixed-duration sliding windows. The analysis uses a suite of common network diagnostics, including the distributions of degrees, distances, and clustering, to track network structure. Several random models that call these diagnostics as parameters help tease them apart as factors from the values of others. Some behaviors are consistent over the entire interval, but most diagnostics indicate that the network's structural evolution is dominated by occasional dramatic shifts in otherwise steady trends. These behaviors are not distributed evenly across the network; stark differences in evolution can be observed between two major subnetworks, loosely thought of as "pure" and "applied", which approximately partition the aggregate. The paper characterizes two major events along the mathematics network trajectory and discusses possible explanatory factors.Comment: 30 pages, 14 figures, 1 table; supporting information: 5 pages, 5 figures; published in Scientometric

Random graphs with arbitrary degree distributions and their applications

Recent work on the structure of social networks and the internet has focussed attention on graphs with distributions of vertex degree that are significantly different from the Poisson degree distributions that have been widely studied in the past. In this paper we develop in detail the theory of random graphs with arbitrary degree distributions. In addition to simple undirected, unipartite graphs, we examine the properties of directed and bipartite graphs. Among other results, we derive exact expressions for the position of the phase transition at which a giant component first forms, the mean component size, the size of the giant component if there is one, the mean number of vertices a certain distance away from a randomly chosen vertex, and the average vertex-vertex distance within a graph. We apply our theory to some real-world graphs, including the world-wide web and collaboration graphs of scientists and Fortune 1000 company directors. We demonstrate that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.Comment: 19 pages, 11 figures, some new material added in this version along with minor updates and correction