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Reporte de Pares: Diseño de Muestreo y Estimaciones Imparciales

Kang Wen1, Jianhong Mou1, Xin Lu1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

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|January 28, 2026
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Resumen
Este resumen es generado por máquina.

El nuevo estimador ECM corregido por la relación de actividad (ECMac) mejora el análisis de redes sociales al proporcionar estimaciones imparciales de la proporción poblacional. Este método mejora la precisión en redes heterogéneas, superando a los métodos tradicionales de muestreo egocéntrico (ECM).

Palabras clave:
relación de actividadredes complejasred del egomuestreo de redesinferencia estadística

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Área de la Ciencia:

  • Análisis de Redes Sociales
  • Inferencia Estadística
  • Ciencias Sociales Computacionales

Sus antecedentes:

  • El método de muestreo egocéntrico (ECM) estima las proporciones poblacionales utilizando reportes de pares, garantizando la privacidad.
  • El ECM convencional está limitado por la suposición de redes homogéneas (grados de nodo uniformes).
  • Las correlaciones atributo-grado en redes heterogéneas sesgan las estimaciones del ECM tradicional.

Objetivo del estudio:

  • Introducir el estimador ECM corregido por la relación de actividad (ECMac) para la inferencia de redes imparciales.
  • Abordar las limitaciones del ECM convencional en redes sociales heterogéneas.
  • Desarrollar un método de preservación de la privacidad para la estimación precisa de la proporción poblacional.

Principales métodos:

  • Reformulación de la estimación de la proporción poblacional en una formulación de espacio de aristas utilizando la reciprocidad de la red.
  • ECMac corrige las dependencias entre los grados y atributos de los nodos.
  • Utiliza solo datos de ego-pares, evitando la necesidad de una estructura de red completa.

Principales resultados:

  • ECMac proporciona estimaciones imparciales y estables en redes heterogéneas.
  • Demostró una reducción de hasta el 70% en el error de estimación en comparación con el ECM convencional.
  • Simulaciones y análisis de redes del mundo real validan el rendimiento de ECMac.

Conclusiones:

  • ECMac ofrece un marco teóricamente fundamentado y prácticamente escalable para el muestreo basado en redes.
  • Mejora la confiabilidad del análisis de redes sociales en diversas estructuras de red.
  • Establece un método robusto para la estimación de atributos poblacionales con preservación de la privacidad.