Exact Recovery of Community Detection in k-Community Gaussian Mixture Model
This work addresses community detection in networks with heterogeneous noise and imbalanced groups, with applications to hypergraphs, but it appears incremental as it extends existing models rather than introducing a fundamentally new approach.
The paper tackles the community detection problem in a Gaussian mixture model with varying perturbation intensities and unequal community sizes, explicitly determining the threshold for exact recovery using maximum likelihood estimation.
We study the community detection problem on a Gaussian mixture model, in which vertices are divided into $k\geq 2$ distinct communities. The major difference in our model is that the intensities for Gaussian perturbations are different for different entries in the observation matrix, and we do not assume that every community has the same number of vertices. We explicitly find the threshold for the exact recovery of the maximum likelihood estimation. Applications include the community detection on hypergraphs.