GEMSEC: Graph Embedding with Self Clustering

By Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton. International Conference on Advances in Social Networks Analysis and Mining, 2018

365 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/59be148a4b5f7e05cb3cb24afa1f6adad2cdfa29

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Citations in the corpus

Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, positive): “Further, clustering can be joined with the learning algorithm while learning a shallow [97] or Graph Convolution [27, 28] embedding model.”