  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 communities. Stochastic block models (SBM) offer a framework to
rigorously define communities, and to detect them using statistical inference
method to distinguish structure from random fluctuations. In this paper, we
introduce an alternative definition of SBM based on edge sampling. We derive
from this definition a quality function to statistically infer the node
partition used to generate a given graph. We then test it on synthetic graphs,
and on the zachary karate club network.
