Continuous Latent Factor Models for Cognitive Social Structure Data

• Activity Number: 627
• Type: Invited
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Abel Rodriguez*
• Companies: UC Santa Cruz
• Keywords: Cognitive Social Structures ; Latent Factor Models

Abstract

Cognitive social structures (CSSs) are a type of social network data that appear often in psychology, sociology and industrial organization applications.  CSSs 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 CSSs 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, "impartial" observer.  Traditionally, CSSs 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 some recent advances in model-based approaches for the analysis of this type of data using continuous latent factor models.