%0 Generic %1 rozemberczki2018gemsec %A Rozemberczki, Benedek %A Davies, Ryan %A Sarkar, Rik %A Sutton, Charles %D 2018 %K cluster embedding graph network social %T GEMSEC: Graph Embedding with Self Clustering %U http://arxiv.org/abs/1802.03997 %X 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.

@misc{rozemberczki2018gemsec, 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.}, added-at = {2020-05-24T17:32:25.000+0200}, author = {Rozemberczki, Benedek and Davies, Ryan and Sarkar, Rik and Sutton, Charles}, biburl = {https://www.bibsonomy.org/bibtex/283e82181b008828d743efa88e070bd9f/parismic}, description = {[1802.03997] GEMSEC: Graph Embedding with Self Clustering}, interhash = {faad8c3891f441da5b1808a0b3e3495d}, intrahash = {83e82181b008828d743efa88e070bd9f}, keywords = {cluster embedding graph network social}, note = {cite arxiv:1802.03997}, timestamp = {2020-05-24T17:32:25.000+0200}, title = {GEMSEC: Graph Embedding with Self Clustering}, url = {http://arxiv.org/abs/1802.03997}, year = 2018 }