Detecting and analyzing dense groups or communities from social and information networks
has attracted immense attention over the last decade due to its enormous applicability in …

The community plays a crucial role in understanding user behavior and network
characteristics in social networks. Some users can use multiple social networks at once for a …

Due to nowadays networks' sizes, the evaluation of a community detection algorithm can
only be done using quality functions. These functions measure different networks/graphs …

Community detection emerged as an important exploratory task in complex networks
analysis across many scientific domains. Many methods have been proposed to solve this …

This article presents an efficient hierarchical clustering algorithm based on a graph traversal
algorithm called LexDFS. This traversal algorithm has the property of going through the …

Graph clustering, also often referred to as network community detection, is the process of
assigning common labels to vertices that are densely connected to each other but sparsely …

Community detection is a technique used to separate graphs into several densely
connected groups of vertices, especially powerful when visualization techniques are …

Dans cette thèse, je commence par présenter une nouvelle caractérisation des
communautés à partir d'un réseau de messages inscrits dans le temps. Je montre que la …

This thesis explores collective decision-making within socio-physics and econophysics,
presenting insights into optimal democratic representation, voters' behaviour, and …

This paper focuses on the silhouette as validity criterion for community structures in
networks, with emphasis on multiplex networks. We propose a versatile definition of the …