Graph embedding is becoming an important method with applications in various areas, including social networks and knowledge graph completion. In particular, Poincar\'e embedding has been proposed to capture the hierarchical structure of graphs, and its effectiveness has been reported. However, most of the existing methods have isometric mappings in the embedding space, and the choice of the origin point can be arbitrary. This fact is not desirable when the distance from the origin is used as an indicator of hierarchy, as in the case of Poincar\'e embedding. In this paper, we propose graph embedding in a metric cone to solve such a problem, and we gain further benefits: 1) we provide an indicator of hierarchical information that is both geometrically and intuitively natural to interpret, 2) we can extract the hierarchical structure from a graph embedding output of other methods by learning additional one-dimensional parameters, and 3) we can change the curvature of the embedding space via a hyperparameter.
翻译:图形嵌入正在成为在各个领域应用的一个重要方法,包括社交网络和知识图的完成。 特别是, Poincar\'e 嵌入已被提议用来捕捉图形的等级结构, 其有效性已被报告。 但是, 大多数现有方法在嵌入空间中都有等量映射, 并且源点的选择可能是任意的。 当从来源到来源的距离被用作等级指标时, 这一事实是不可取的, 比如Poincar\'e 嵌入。 在本文中, 我们提议用图嵌入一个公制锥体来解决这个问题, 我们获得了进一步的好处:(1) 我们提供了一种等级信息的指标, 它既具有几何学性质, 也具有直观性来解释, 2) 我们可以通过学习额外的一维参数从图表嵌入其他方法的输出中提取等级结构, 3) 我们可以通过超参数来改变嵌入空间的曲线。