GEMSEC：自聚类图嵌入
GEMSEC: Graph Embedding with Self Clustering
摘要：
现代图嵌入程序可以有效地处理具有数百万个节点的图。在本文中，我们提出了GEMSEC -一个图形嵌入算法，学习聚类的节点，同时计算其嵌入。GEMSEC是基于序列的图嵌入领域早期工作的一般扩展。GEMSEC将节点放置在抽象特征空间中，其中顶点特征最小化保留采样顶点邻域的负对数似然，并且通过机器学习正则化结合已知的社交网络属性。我们提出了两个新的社会网络数据集，并表明，通过同时考虑嵌入和聚类问题的社会属性，GEMSEC提取高质量的集群竞争或优于其他社区发现算法。在实验中，该方法被发现是计算效率和强大的超参数的选择。
英文摘要：
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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