Before revision:  Due to their principled characterization and interpretation , probabilistic generative models (PGMs) have become the mainstream methods for community detection in attributed networks. Most existing PGMs require the attributes to be categorical or Gaussian distributed.
After revision:  Due to their principled characterization and interpretation , probabilistic generative models (PGMs) have become the mainstream fusion method due to their principled characterization and interpretation. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to existing PGMs that require attributes to be categorical or Gaussian distributed.