  In this paper, we study how to simultaneously learn two highly correlated
tasks of graph analysis, i.e., community detection and node representation
learning. We propose an efficient generative model called VECoDeR for jointly
learning Variational Embeddings for Community Detection and node
Representation. VECoDeR assumes that every node can be a member of one or more
communities. The node embeddings are learned in such a way that connected nodes
are not only "closer" to each other but also share similar community
assignments. A joint learning framework leverages community-aware node
embeddings for better community detection. We demonstrate on several graph
datasets that VECoDeR effectively out-performs many competitive baselines on
all three tasks i.e. node classification, overlapping community detection and
non-overlapping community detection. We also show that VECoDeR is
computationally efficient and has quite robust performance with varying
hyperparameters.
