Compressibility of Network Opinion and Spread States in the Laplacian-Eigenvector Basis


Abstract:

Using three case studies, we examine whether snapshot data from network opinion-evolution and spread processes are compressible in the Laplacian-eigenvector basis, in the sense that each snapshot can be approximated well using a (possibly different) small set of basis vectors. The first case study is concerned with a linear consensus model that is subject to a stochastic input at an unknown location; both empirical and formal analyses are used to characterize compressibility. Second, compressibility of state snapshots for a stochastic voter model is assessed via an empirical study. Finally, compressibility is studied for state-level daily COVID-19 positivity-rate data. The three case studies indicate that state snapshots from opinion-evolution and spread processes allow terse representations, which nevertheless capture their rich propagative dynamics.

Año de publicación:

2021

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

      Áreas temáticas de Dewey:

      • Ciencias de la computación
      Procesado con IAProcesado con IA

      Objetivos de Desarrollo Sostenible:

      • ODS 9: Industria, innovación e infraestructura
      • ODS 17: Alianzas para lograr los objetivos
      • ODS 3: Salud y bienestar
      Procesado con IAProcesado con IA

      Contribuidores: