SSDE-Cluster: Fast Overlapping Clustering of Networks Using Sampled Spectral Distance Embedding and GMMs

Clustering social networks is vital to understanding online interactions and influence. This task becomes more difficult when communities overlap, and when the social networks become extremely large. We present an efficient algorithm for constructing overlapping clusters, (approximately linear). The algorithm first embeds the graph and then performs a metric clustering using a Gaussian Mixture Model (GMM). We evaluate the algorithm on the DBLP paper-paper network which consists of about 1 million nodes and over 30 million edges, we can cluster this network in under 20 minutes on a modest single CPU machine.