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GEMSEC: Graph Embedding with Self Clustering

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Original languageEnglish
Title of host publicationProceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2019
Number of pages8
Publication statusAccepted/In press - 20 Jun 2019
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2019 - Marriot Downtown Hotel, Vancouver, Canada
Duration: 27 Aug 201930 Aug 2019
Conference number: 11
http://asonam.cpsc.ucalgary.ca/2019/

Conference

ConferenceIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2019
Abbreviated titleASONAM 2019
CountryCanada
CityVancouver
Period27/08/1930/08/19
Internet address

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.

    Research areas

  • community detection, clustering, node embedding, network embedding, feature extraction

ID: 102405100