  Modern graph embedding procedures can efficiently process graphs with
millions of nodes. In this paper, we propose GEMSEC -- a graph embedding
algorithm which learns a clustering of the nodes simultaneously with computing
their embedding. GEMSEC is a general extension of earlier work in the domain of
sequence-based graph embedding. GEMSEC places nodes in an abstract feature
space where the vertex features minimize the negative log-likelihood of
preserving sampled vertex neighborhoods, and it incorporates known social
network properties through a machine learning regularization. We present two
new social network datasets and show that by simultaneously considering the
embedding and clustering problems with respect to social properties, GEMSEC
extracts high-quality clusters competitive with or superior to other community
detection algorithms. In experiments, the method is found to be computationally
efficient and robust to the choice of hyperparameters.
