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    [0] => stdClass Object
        (
            [journal] => stdClass Object
                (
                    [id_jnl] => 88
                )

        )

    [1] => stdClass Object
        (
            [section] => stdClass Object
                (
                    [section] => 278
                )

        )

    [2] => stdClass Object
        (
            [title] => Array
                (
                    [0] => Contribution of graph theory to the understanding of social dynamics@en
                    [1] => Aporte de la teoría de grafos a la comprensión de las dinámicas sociales@es
                )

        )

    [3] => stdClass Object
        (
            [abstract] => Array
                (
                    [0] => This review article examines how graph theory has contributed significantly to understanding social dynamics by discussing its applicability to social network analysis. A predominantly qualitative study was carried out, with a scoping review design aimed at understanding how this set of tools facilitates studying human interactions and the structure of social groups. The main results highlighted key concepts such as centrality, clusters, connectivity, and network resilience, as well as their applications to analyzing social phenomena such as information diffusion, opinion formation, disease spread, and social cohesion. In addition, the methodological challenges and limitations of graph theory were examined, to propose future directions for interdisciplinary research that delve into the interaction between mathematics and social sciences, especially Social Psychology applied to collective behavior and the social network analysis.@en
                    [1] => Este artículo de revisión panorámica examina cómo la teoría de grafos ha contribuido significativamente a la comprensión de las dinámicas sociales, mediante la discusión de la aplicabilidad de misma al análisis de redes sociales. Se llevó a cabo un estudio predominantemente cualitativo, con un diseño de scoping review dirigido a la comprensión de cómo este conjunto de herramientas facilita el estudio de las interacciones humanas y la estructura de los grupos sociales. Los principales resultados destacaron conceptos clave como la centralidad, los clústeres, la conectividad y la resiliencia de las redes, así como sus aplicaciones al análisis de fenómenos sociales como la difusión de información, la formación de opiniones, la propagación de enfermedades y la cohesión social. Además, se examinaron los desafíos metodológicos y las limitaciones de la teoría de grafos, con el propósito de proponer direcciones futuras para la investigación interdisciplinaria que profundicen en la interacción entre las matemáticas y las ciencias sociales, especialmente la Psicología Social aplicada al comportamiento colectivo y el análisis de redes sociales.@es
                )

        )

    [4] => stdClass Object
        (
            [author] => Array
                (
                    [0] => Alfredo Javier Pérez Gamboa
                )

        )

    [5] => stdClass Object
        (
            [subject] => Array
                (
                    [0] => Network analysis@en
                    [1] => Centrality@en
                    [2] => Social cohesion@en
                    [3] => Information diffusion@en
                    [4] => Social dynamics@en
                    [5] => Human interaction@en
                    [6] => Social networks@en
                    [7] => Graph theory@en
                    [8] => Análisis de redes@es
                    [9] => Centralidad@es
                    [10] => Cohesión social@es
                    [11] => Difusión de información@es
                    [12] => Dinámicas sociales@es
                    [13] => Interacción humana@es
                    [14] => Redes sociales@es
                    [15] => Teoría de grafos@es
                )

        )

    [6] => stdClass Object
        (
            [source] => stdClass Object
                (
                    [vol] => 4
                    [nr] => 
                    [year] => 2023
                    [theme] => 
                )

        )

    [7] => stdClass Object
        (
            [datePub] => Array
                (
                    [0] => 2023-08-18
                )

        )

    [8] => stdClass Object
        (
            [DOI] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => DOI
                            [value] => Array
                                (
                                    [0] => 10.47909/awari.51
                                )

                        )

                )

        )

    [9] => stdClass Object
        (
            [http] => Array
                (
                    [0] => stdClass Object
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                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://awari.pro-metrics.org/index.php/a/article/view/51
                                )

                        )

                    [1] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://awari.pro-metrics.org/index.php/a/article/view/51/48
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        )

    [10] => stdClass Object
        (
            [language] => Array
                (
                    [0] => es
                )

        )

    [11] => stdClass Object
        (
            [license] => Array
                (
                    [0] => Copr
                    [1] => by-nc/4.0
                )

        )

)