Machine Learning Pour La Détection Des Communautés
No Thumbnail Available
Date
2024
Authors
Wafa LOUAFI
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
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.