“Polarization in Opinion Diffusion and Active Sensing of Social Network Systems”
Dr. Anna Scaglione, Ph.D.
Arizona State University
Date: September 30, 2016
Abstract:
Time: 11:45 a.m.
Place: EBII 1230
Studies on modeling social influence date as far back as the 18th century. Originally these studies
supported the so called notion of "wisdom of the crowds, i.e. the idea that, by sharing opinions,
groups could form better decisions than those of any individual agent.Experimental evidence has
later shown the existence of pathological herding phenomena and confirming how information
diffusion can lead to irrational group behavior and manipulation from few stubborn social agents.
In this talk we will first briefly review the salient properties of the key mathematical models capture
polarization phenomena in social agents interactions: the so called DeGroot model of consensus
building in the presence of stubborn agents and and Hegselmann-Krause/Deffuant model model of
bounded confidence, which capture herding phenomena and polarization. We will then introduce
the "social radar" problem of identifying the trust system that characterizes the dynamics of the
opinions in a network with stubborn agents and present our findings and results on synthetic and
real data.
Bio:
Anna Scaglione is professor in electrical and computer engineering at Arizona State University since
2015. She was previously on the faculty of UC Davis, Cornell and UNM.Dr. Scaglione’s expertise is in
statistical signal processing, communication systems and more recently in power systems. Her work
focuses on modeling and data analysis for complex network systems, from information systems,
sensor networks and intelligent infrastructure for energy delivery, to social networks.
Dr. Scaglione was elected an IEEE fellow in 2011. She received the 2000 IEEE Signal Processing
Transactions Best Paper Award and more recently was honored for the 2013, IEEE Donald G. Fink
Prize Paper Award for the best review paper in that year in the IEEE publications, her work with her
student earned 2013 IEEE Signal Processing Society Young Author Best Paper Award.