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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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TaCD: Detección de Comunidades Consciente del Equipo Basada en Modularidad Multi-vista

Chengzhou Fu1, Feiyi Tang2, Lingzhi Hu3,4

  • 1College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo algoritmo, Detección de Comunidades Consciente del Equipo (TaCD), para redes sociales. TaCD incorpora eficazmente información del equipo, superando a los métodos existentes en tareas de detección de comunidades.

Palabras clave:
Louvain GeneralizadoSCHOLATdetección de comunidadesmulti-vistaconsciente del equipo

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Área de la Ciencia:

  • Ciencia de redes
  • Análisis de redes sociales
  • Minería de datos

Sus antecedentes:

  • La detección de comunidades es crucial en la ciencia de redes.
  • Los métodos existentes a menudo ignoran información vital del equipo en las redes sociales.
  • Las estructuras de equipo influyen significativamente en la formación y evolución de las comunidades.

Objetivo del estudio:

  • Proponer un novedoso algoritmo de detección de comunidades que integre información del equipo.
  • Desarrollar un modelo de red multi-vista que incorpore interacciones de usuarios y estructuras de equipo.
  • Introducir un nuevo conjunto de datos para evaluar la detección de comunidades consciente del equipo.

Principales métodos:

  • Se construyó una red multi-vista con interacciones de usuarios (vista de usuario) e información del equipo (vista de equipo).
  • Se utilizó la similitud de Jaccard para el acoplamiento entre vistas para medir la consistencia.
  • Se empleó la modularidad multi-vista y el enfoque Generalized Louvain para la optimización.

Principales resultados:

  • El algoritmo propuesto de Detección de Comunidades Consciente del Equipo (TaCD) identifica comunidades de manera efectiva.
  • TaCD demuestra un rendimiento superior en comparación con los algoritmos existentes en conjuntos de datos de referencia.
  • El nuevo conjunto de datos SCHOLAT facilita la investigación en la detección de comunidades consciente del equipo.

Conclusiones:

  • La integración de información del equipo mejora la precisión de la detección de comunidades en redes sociales.
  • TaCD proporciona un marco robusto para descubrir estructuras de comunidades complejas.
  • El algoritmo y el conjunto de datos desarrollados contribuyen significativamente al campo de la ciencia de redes.