Articles producció científica> Enginyeria Química

Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks

  • Datos identificativos

    Identificador: imarina:9332610
    Autores:
    Sales-Pardo MMariné-Tena AGuimerà R
    Resumen:
    Complex networked systems often exhibit higher-order interactions, beyond dyadic interactions, which can dramatically alter their observed behavior. Consequently, understanding hypergraphs from a structural perspective has become increasingly important. Statistical, group-based inference approaches are well suited for unveiling the underlying community structure and predicting unobserved interactions. However, these approaches often rely on two key assumptions: that the same groups can explain hyperedges of any order and that interactions are assortative, meaning that edges are formed by nodes with the same group memberships. To test these assumptions, we propose a group-based generative model for hypergraphs that does not impose an assortative mechanism to explain observed higher-order interactions, unlike current approaches. Our model allows us to explore the validity of the assumptions. Our results indicate that the first assumption appears to hold true for real networks. However, the second assumption is not necessarily accurate; we find that a combination of general statistical mechanisms can explain observed hyperedges. Finally, with our approach, we are also able to determine the importance of lower and high-order interactions for predicting unobserved interactions. Our research challenges the conventional assumptions of group-based inference methodologies and broadens our understanding of the underlying structure of hypergraphs.
  • Otros:

    Autor según el artículo: Sales-Pardo M; Mariné-Tena A; Guimerà R
    Departamento: Enginyeria Química
    Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
    Palabras clave: Stochastic block models Probabilistic inference Link prediction Higher-order interactions Complex networks
    Resumen: Complex networked systems often exhibit higher-order interactions, beyond dyadic interactions, which can dramatically alter their observed behavior. Consequently, understanding hypergraphs from a structural perspective has become increasingly important. Statistical, group-based inference approaches are well suited for unveiling the underlying community structure and predicting unobserved interactions. However, these approaches often rely on two key assumptions: that the same groups can explain hyperedges of any order and that interactions are assortative, meaning that edges are formed by nodes with the same group memberships. To test these assumptions, we propose a group-based generative model for hypergraphs that does not impose an assortative mechanism to explain observed higher-order interactions, unlike current approaches. Our model allows us to explore the validity of the assumptions. Our results indicate that the first assumption appears to hold true for real networks. However, the second assumption is not necessarily accurate; we find that a combination of general statistical mechanisms can explain observed hyperedges. Finally, with our approach, we are also able to determine the importance of lower and high-order interactions for predicting unobserved interactions. Our research challenges the conventional assumptions of group-based inference methodologies and broadens our understanding of the underlying structure of hypergraphs.
    Áreas temáticas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Matemática / probabilidade e estatística Interdisciplinar Geografía Geociências General o multidisciplinar Farmacia Engenharias iv Engenharias iii Engenharias ii Engenharias i Educação física Ciencias sociales Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Antropologia / arqueologia Anthropology
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat
    Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525
    Fecha de alta del registro: 2024-07-27
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.pnas.org/doi/10.1073/pnas.2303887120
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Proceedings Of The National Academy Of Sciences Of The United States Of America. 120 (50): e2303887120-e2303887120
    Referencia de l'ítem segons les normes APA: Sales-Pardo M; Mariné-Tena A; Guimerà R (2023). Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks. Proceedings Of The National Academy Of Sciences Of The United States Of America, 120(50), e2303887120-e2303887120. DOI: 10.1073/pnas.2303887120
    DOI del artículo: 10.1073/pnas.2303887120
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2023
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Multidisciplinary,Multidisciplinary Sciences
    Stochastic block models
    Probabilistic inference
    Link prediction
    Higher-order interactions
    Complex networks
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Química
    Psicología
    Odontología
    Multidisciplinary sciences
    Multidisciplinary
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geografía
    Geociências
    General o multidisciplinar
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Educação física
    Ciencias sociales
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
    Antropologia / arqueologia
    Anthropology
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