Butts, Carter T. (2008). A Relational Event Framework for Social Action. Sociological Methodology, 38(1):155-200.
Butts, Carter. 2008. Social network analysis: A methodological introduction Asian Journal of Social Psychology 11(1):13-31.
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, Martina Morris. 2008 statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data Journal of Statistical Software 24(1):1-11.
2010-2011 IMBS Colloquiua
CARTER T. BUTTS
Department of Sociology UCI
“Some Simple Constraints on Social Structure”
Thursday, May 19 SSPA 2112 4:00 – 5:00 p.m.
While social networks are often considered to be "complex systems," many of the deepest insights into the nature of social structure come from relatively simple things. In part, this stems from the fact that the set of possible social structures is heavily constrained by the intrinsic dependence among structural features: fixing one feature of a social network necessarily constrains other features of that network. While this intuition extends back at least to the work of Simmel, its far-reaching implications are often overlooked. Likewise, basic contextual factors such as population size, geography, and heterogeneity can shape network structure to a much greater degree than is often appreciated. In this talk, I outline some of the important -- and often surprising -- ways in which "simple" constraints can enhance our understanding of social networks. I will also highlight some examples of such phenomena which persist even within highly disrupted social settings. Finally, I will argue that there is much that we do not currently understand regarding the basic factors which influence social structure, and that there is a great deal of room for the further study of allegedly simple constraints.
CARTER T. BUTTS Department of Sociology UCI
“An Inferentially Tractable Behavioral Micro-foundation for Cross-sectional Network Models”
Thursday, May 6 SSPA 2112 4:00 – 5:00 p.m.
Abstract: Models for cross-sectional network data have become increasingly well-developed in recent decades, and are widely used. Questions remain, however, about the connection between such cross-sectional models and the behavioral processes from which the corresponding networks were presumably generated. Here, we present a behavioral micro-foundation for a broad class of cross-sectional models, based on a continuous time stochastic choice mechanism. As we show, the equilibrium behavior of this process under appropriate conditions can be expressed in exponential family form, allowing estimation of individual preferences using existing methods. We also show that the equilibrium impact of alternative edge formation rules can be expressed as an deterministic offset term in the exponential family representation of the corresponding model, allowing for linear separation of structural biases due to actor preferences, versus biases due to formation rules. We demonstrate the applicability of the approach via an analysis of advice-seeking behavior among managers within a high-tech firm, providing evidence of preferences for seeking advice from “upstream” sources net of covariate and other effects.