2 citations · 5 across the 4 of their papers we have counts for
10 papers
Graph model selection by edge probability sequential inference
Louis Duvivier, Rémy Cazabet, Céline Robardet
Graphs are widely used for describing systems made up of many interacting components and for understanding the structure of their interactions. Various statistical models exist, wh…
Edge based stochastic block model statistical inference
Louis Duvivier, Rémy Cazabet, Céline Robardet
Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communi…
Data compression to choose a proper dynamic network representation
Remy Cazabet
Dynamic network data are now available in a wide range of contexts and domains. Several representation formalisms exist to represent dynamic networks, but there is no well-known me…
Evaluating Community Detection Algorithms for Progressively Evolving Graphs
Remy Cazabet, Souaad Boudebza, Giulio Rossetti
Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, w…
Minimum entropy stochastic block models neglect edge distribution heterogeneity
Louis Duvivier, Rémy Cazabet, Céline Robardet
The statistical inference of stochastic block models as emerged as a mathematicaly principled method for identifying communities inside networks. Its objective is to find the node…
Challenges in Community Discovery on Temporal Networks
Remy Cazabet, Giulio Rossetti
Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifyi…