Recently, hyperbolic space has risen as a promising alternative for semi-supervised graph representation learning. Many efforts have been made to design hyperbolic versions of neural network operations. However, the inspiring geometric properties of this unique geometry have not been fully explored yet. The potency of graph models powered by the hyperbolic space is still largely underestimated. Besides, the rich information carried by abundant unlabelled samples is also not well utilized. Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning. More specifically, we put forward a novel Hyperbolic Graph Contrastive Learning (HGCL) framework which learns node representations through multiple hyperbolic spaces to implicitly capture the hierarchical structure shared between different views. Then, we design a hyperbolic position consistency (HPC) constraint based on hyperbolic distance and the homophily assumption to make contrastive learning fit into hyperbolic space. Experimental results on multiple real-world datasets demonstrate the superiority of the proposed HGCL as it consistently outperforms competing methods by considerable margins for the node classification task.
翻译:最近,双曲线空间作为半受监督的图形演示学习的一种有希望的替代方法,已经上升了超曲线空间,作为半受监督的图形演示学习的一种有希望的替代方法。我们做出了许多努力来设计神经网络运行的双曲线版本。然而,这一独特的几何测量学的激励性几何特性尚未得到充分探讨。由超曲线空间驱动的图形模型的功效仍然被大大低估。此外,大量无标签样本所携带的丰富信息也没有被很好地利用。受最近活跃的和新出现的自我监督的学习启发,我们在本研究中试图利用对比学习的优势来增强超曲线图形模型模型的表达力。更具体地说,我们提出了一个新型的超曲线图形对比学习(HGCL)框架,通过多个双曲线空间来学习节点表达,以隐含地捕捉不同观点之间共享的等级结构。然后,我们设计了一个基于超曲线距离的超曲线定位(HPC)限制和单式假设,以使对比性学习适合超曲线空间。关于多个真实世界数据设置的实验结果显示HGCL(HGCL)的优越性,因为它以相当的平差方式持续地展示了相互竞争的任务。