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  • Lifting Majority to Unanimity in Opinion Diffusion

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    Wang444961-Published.pdf (285.9Kb)
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    Author(s)
    Zhuang, Zhiqiang
    Wang, Kewen
    Wang, Junhu
    Zhang, Heng
    Wang, Zhe
    Gong, Zhiguo
    Griffith University Author(s)
    Wang, John
    Wang, Zhe
    Wang, Kewen
    Year published
    2020
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    Abstract
    In this paper, we study an information exchange process in which a network of individuals exchanges a binary opinion. In the process, the individuals change their opinions only if a majority of their neighbours have the opposite opinion and they do it synchronously. Motivated by applications in multiagent systems, distributed computing, and social science, our goal is to derive graph-theoretic features of the network that guarantee whenever a majority of individuals initially have the same opinion, they will eventually spread the opinion to all individuals. We tackle the problem by first introducing a graph-theoretic notion ...
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    In this paper, we study an information exchange process in which a network of individuals exchanges a binary opinion. In the process, the individuals change their opinions only if a majority of their neighbours have the opposite opinion and they do it synchronously. Motivated by applications in multiagent systems, distributed computing, and social science, our goal is to derive graph-theoretic features of the network that guarantee whenever a majority of individuals initially have the same opinion, they will eventually spread the opinion to all individuals. We tackle the problem by first introducing a graph-theoretic notion called controlling set which is capable of characterising the information exchange process and, by exploiting the notion, we obtain a series of lower and upper bounds on the in-degree of vertices as well as lower bound on the size of certain neighbourhoods for guaranteeing the majority to unanimity behaviour.
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    Conference Title
    Frontiers in Artificial Intelligence and Applications
    Volume
    325
    DOI
    https://doi.org/10.3233/FAIA200101
    Copyright Statement
    © 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/401896
    Collection
    • Conference outputs

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