- Buchanan 1930
Title: Title: Analyzing cognitive social structures
Cognitive social structures (CSSs) are a type of social network data that appear often in psychology, sociology and industrial organization applications. Cognitive social structures involve a collection of networks, each one of them reflecting the perceptions of an individual about the interactions among all members of the community. This makes cognitive social structures richer than other forms of social networks that only reflect the subject perception about his direct links but not those about the links about third parties, or are collected from the perspective of a single, “objective” observer.
Traditionally, cognitive social structures have been analyzed by either collapsing the multiple networks into some sort of "consensus" network, or by analyzing different "slices" independently. In this talk we discuss two classes of models used to generate insights from CSS data. In the first part of the talk, we use a hierarchical embedding of the networks into a continuous latent space along with carefully constructed zero-inflated priors to explore the perceptual agreement between individuals and the group consensus. In the second part of the talk we discuss a novel class of hierarchical blockmodels that uses Chinese Restaurant Process priors and fragmentation-coagulation processes to identify conserved motifs that are preserved across all observers. This is joint work with Juan Sosa (Universidad del Externado, Colombia) and Perla Reyes (Kansas State University).
Abel Rodriguez is a Professor of Statistics and Associate Dean for Graduate Affairs at the Baskin School of Engineering at the University of California, Santa Cruz. He is also the Associate Director of the Center for Data, Discovery and Decisions (D3) at UC Santa Cruz. He develops statistical methods for complex problems in biology, social sciences and engineering. His research interests include Bayesian statistics and machine learning, specially nonparametric methods, spatial temporal models, network analysis and extreme value theory.