3 citations · 5 across the 4 of their papers we have counts for
4 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…
Graph space: using both geometric and probabilistic structure to evaluate statistical graph models
Louis Duvivier, Rémy Cazabet, Céline Robardet
Statistical graph models aim at modeling graphs as random realization among a set of possible graphs. One issue is to evaluate whether or not a graph is likely to have been generat…
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…