Estructura comunitaria de la red de neuronas de C. Elegans
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[ES] Este trabajo tiene tres objetivos principales. En primer lugar estudiamos la estructura
comunitaria del conectoma del nematodo C. elegans por medio de dos técnicas diferentes:
el algoritmo de Lovaina y los modelos de bloques estocásticos. Además, estudiamos si existe
alguna relación entre la distribución modular obtenida y la partición del conectoma en
ganglios y grupos funcionales. En segundo lugar, mediante la implementación de estos
algoritmos de detección de la estructura comunitaria sobre redes de control, cuya distribución
modular es conocida, verificamos en qué casos los métodos empleados son más eficaces.
Por último, tratamos de definir sobre cada red un núcleo, es decir, un conjunto de nodos
encargados de su correcto funcionamiento, de manera que si este desaparece de la red, esta
no podría realizar las funciones para las que fue diseñada.
[EN] This work has three different objectives. First of all, we study the C. elegans connectome and, using different techniques like the Louvain algorithm and stochastic block modeling technics (SBM), we try to find his community structure. Moreover, we study if there is a relationship between the obtained modular distribution and the partitioning of the connectome in ganglia and functional groups. Secondly, applying these community detection algorithms to benchmark networks as Internet2 and Ravasz’s hierarchical graph for which we know it’s community structure, we check in which cases each algorithm works better. Finally, we try to define for each network a core, that is, we try to set a collection of nodes that are responsible for each network to make it work properly. Moreover, if we remove this subset from the network, it should stop working.
[EN] This work has three different objectives. First of all, we study the C. elegans connectome and, using different techniques like the Louvain algorithm and stochastic block modeling technics (SBM), we try to find his community structure. Moreover, we study if there is a relationship between the obtained modular distribution and the partitioning of the connectome in ganglia and functional groups. Secondly, applying these community detection algorithms to benchmark networks as Internet2 and Ravasz’s hierarchical graph for which we know it’s community structure, we check in which cases each algorithm works better. Finally, we try to define for each network a core, that is, we try to set a collection of nodes that are responsible for each network to make it work properly. Moreover, if we remove this subset from the network, it should stop working.
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Traballo Fin de Grao en Matemáticas. Curso 2019-2020
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