Community Detection on Euclidean Random Graphs

Understand

We study the problem of community detection (CD) on Euclidean random geometric graphs where each vertex has two latent variables: a binary community label and a $\mathbb{R}^d$ valued location label which forms the support of a Poisson point process of intensity $\lambda$.

• A random graph is then drawn with edge probabilities dependent on both the community and location labels.
• In contrast to the stochastic block model (SBM) that has no location labels, the resulting random graph contains many more short loops due to the geometric embedding.
• We consider the recovery of the community labels, partial and exact, using the random graph and the location labels.