Publication: Modelos en redes sociales
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2022-09
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Abstract
Las redes sociales constituyen una parte de nuestra sociedad, entenderlas puede ayudar a mejorar ciertos procesos y optimizar resultados desde un punto de vista de transmisión de información, económico o social, ya que estas a su vez están compuestas de diferentes comunidades. El presente trabajo se centra en grafos generados aleatoriamente y no dirigidos, analizando las principales características: agrupamiento, asortatividad, medidas centrales, comunidades, etc. Además de cálculos analíticos, el trabajo se presenta con diferentes gráficos que permiten visualizar y comprender mejor los resultados, también se contrasta con diferentes conjuntos de datos.
The following project aims to analyze social network models and understand how they work, since social networks are a part of our society, understanding them can help to improve certain processes and optimize results from a point of view of information transmission, economic or social since these, in turn, are composed of different communities. The present TFG focuses on randomly generated and undirected graphs, analyzing the main characteristics: clustering, assortativity, central measures, and communities, ..... In addition to analytical calculations, the work is presented with different graphs that allow better visualization and understanding of the results, and it is also contrasted with different datasets.
The following project aims to analyze social network models and understand how they work, since social networks are a part of our society, understanding them can help to improve certain processes and optimize results from a point of view of information transmission, economic or social since these, in turn, are composed of different communities. The present TFG focuses on randomly generated and undirected graphs, analyzing the main characteristics: clustering, assortativity, central measures, and communities, ..... In addition to analytical calculations, the work is presented with different graphs that allow better visualization and understanding of the results, and it is also contrasted with different datasets.