GEMSEC: Graph Embedding with Self Clustering

Understand

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.