Opinion Diffusion in Similarity-Driven Networks

Edoardo Baccini; Zoé Christoff; Rineke Verbrugge

Opinion Diffusion in Similarity-Driven Networks

Artificial Intelligence

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Abstract

There exists a variety of logics of social networks
already, capturing either threshold-based diffusion
dynamics, or similarity-driven social network structure
dynamics, or even both happening at different times.
This paper introduces a novel logical framework to reason
about diffusion and network change happening at the
same time. Our main contributions are twofold: first, we
provide a sound and complete logic for this “synchronic”
perspective; second, we consider this synchronic dynamics
for a unique particularly divisive issue and characterize
the models that stabilize, before discussing salient
properties of stable models with multiple issues.

Original language	English
Publication status	Unpublished - 2022
Event	14th Conference on Logic and the Foundations of Game and Decision Theory - University of Groningen, Groningen, Netherlands Duration: 20-Jul-2022 → 22-Jul-2022 Conference number: 14 https://sites.google.com/rug.nl/loft2020/home

Conference

Conference	14th Conference on Logic and the Foundations of Game and Decision Theory
Abbreviated title	LOFT 2022
Country/Territory	Netherlands
City	Groningen
Period	20/07/2022 → 22/07/2022
Internet address	https://sites.google.com/rug.nl/loft2020/home

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title = "Opinion Diffusion in Similarity-Driven Networks",

abstract = "There exists a variety of logics of social networks already, capturing either threshold-based diffusion dynamics, or similarity-driven social network structure dynamics, or even both happening at different times. This paper introduces a novel logical framework to reason about diffusion and network change happening at the same time. Our main contributions are twofold: first, we provide a sound and complete logic for this “synchronic” perspective; second, we consider this synchronic dynamics for a unique particularly divisive issue and characterize the models that stabilize, before discussing salient properties of stable models with multiple issues.",

author = "Edoardo Baccini and Zo{\'e} Christoff and Rineke Verbrugge",

year = "2022",

language = "English",

note = "14th Conference on Logic and the Foundations of Game and Decision Theory, LOFT 2022 ; Conference date: 20-07-2022 Through 22-07-2022",

url = "https://sites.google.com/rug.nl/loft2020/home",

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TY - CONF

T1 - Opinion Diffusion in Similarity-Driven Networks

AU - Baccini, Edoardo

AU - Christoff, Zoé

AU - Verbrugge, Rineke

N1 - Conference code: 14

PY - 2022

Y1 - 2022

N2 - There exists a variety of logics of social networks already, capturing either threshold-based diffusion dynamics, or similarity-driven social network structure dynamics, or even both happening at different times. This paper introduces a novel logical framework to reason about diffusion and network change happening at the same time. Our main contributions are twofold: first, we provide a sound and complete logic for this “synchronic” perspective; second, we consider this synchronic dynamics for a unique particularly divisive issue and characterize the models that stabilize, before discussing salient properties of stable models with multiple issues.

AB - There exists a variety of logics of social networks already, capturing either threshold-based diffusion dynamics, or similarity-driven social network structure dynamics, or even both happening at different times. This paper introduces a novel logical framework to reason about diffusion and network change happening at the same time. Our main contributions are twofold: first, we provide a sound and complete logic for this “synchronic” perspective; second, we consider this synchronic dynamics for a unique particularly divisive issue and characterize the models that stabilize, before discussing salient properties of stable models with multiple issues.

M3 - Paper

T2 - 14th Conference on Logic and the Foundations of Game and Decision Theory

Y2 - 20 July 2022 through 22 July 2022

ER -

Opinion Diffusion in Similarity-Driven Networks

Abstract

Conference

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