Browsing by Author "Wafa LOUAFI"

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    Machine Learning Pour La Détection Des Communautés
    (University of Batna 2, 2024) Wafa LOUAFI
    The burgeoning field of social network analysis has garnered considerable attention, with a particular focus on the critical area of community detection. Communities, construed as clusters of closely interconnected nodes with weaker ties to the broader network, play a pivotal role in understanding the evolution of network structures, an essential aspect of network analysis. This thesis conducts a comprehensive exploration into community detection algorithms and unsupervised learning methods, subsequently delving into the application of machine learning techniques in this domain. Two distinct methods are presented: one focused on detecting overlapping communities and the other on identifying disjoint communities. Notably, our methods involve the selection of vital nodes through subgraphs, operating individually on each node, while the clustering process itself occurs globally across the entire network. The implementation leverages various unsupervised machine learning techniques, including hierarchical clustering and k-means, showcasing efficiency and ease of implementation. The standout feature of our methods lies in their demonstrated superiority, achieving enhanced accuracy and performance compared to contemporary methodologies. Substantiating this claim, meticulous evaluations were conducted on both real and synthetic network datasets. Beyond these achievements, the research opens avenues for future exploration in understanding the broader implications and applications of community detection within evolving network structures.