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SUMMARY:Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks - O
 lga Zaghen\, University of Trento
DTSTART:20230529T153000Z
DTEND:20230529T163000Z
UID:TALK201931@talks.cam.ac.uk
CONTACT:Pietro Lio
DESCRIPTION:The study of opinion dynamics is an intriguing and challenging
  field that has attracted researchers from various disciplines. Opinion dy
 namics models aim to capture the intricate and dynamic nature of social in
 teractions that shape the formation and evolution of opinions in human soc
 ieties\, and they have been proven valuable in investigating a wide range 
 of phenomena\, including political polarization\, rumor propagation and em
 ergence of consensus. Recently\, there has been a growing interest in empl
 oying computational tools to model opinion dynamics\, and within this real
 m\, sheaf theory has emerged as a powerful mathematical framework. Sheaf t
 heory enables the study of complex systems with both local and global inte
 ractions\, treating opinions as mathematical entities associated with netw
 ork nodes.\n\nBounded confidence\, in the context of opinion dynamics\, re
 fers to a model where individuals are willing to adjust their opinions onl
 y if others' opinions are sufficiently similar to their own. By incorporat
 ing bounded confidence into sheaf theory\, it becomes possible to model an
 d comprehend the emergence of opinion clusters\, polarization\, and the co
 nvergence or divergence of opinions within intricate social networks. An i
 ntriguing question arises: how does the integration of bounded confidence 
 dynamics into a Sheaf Neural Network affect its expressiveness\, signal di
 ffusion\, and ultimately its performance? Furthermore\, what unique proper
 ties does it offer?
LOCATION:Lecture Theatre 2\, Computer Laboratory\, William Gates Building
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