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arXiv · 1802.03997

GEMSEC: Graph Embedding with Self Clustering

Benedek Rozemberczki·Ryan Davies·Rik Sarkar·Charles Sutton
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Abstract

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.

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Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton. 2018-02-12. GEMSEC: Graph Embedding with Self Clustering. https://arxiv.org/abs/1802.03997

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