Variational Embeddings for Community Detection and Node Representation
This work provides an incremental improvement for researchers and practitioners working on graph analysis tasks like community detection and node representation learning.
This paper introduces VECoDeR, a generative model that jointly learns community detection and node representation. It assumes nodes can belong to multiple communities and learns embeddings where connected nodes are closer and share community assignments, outperforming several baselines on node classification and overlapping/non-overlapping community detection.
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.