Unsupervised graph-level anomaly detection (UGAD) has garnered increasing
attention in recent years due to its significance. However, most existing
methods only rely on traditional graph neural networks to explore pairwise
relationships but such kind of pairwise edges are not enough to describe
multifaceted relationships involving anomaly. There is an emergency need to
exploit node group information which plays a crucial role in UGAD. In addition,
most previous works ignore the global underlying properties (e.g., hierarchy
and power-law structure) which are common in real-world graph datasets and
therefore are indispensable factors on UGAD task. In this paper, we propose a
novel Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly
Detection (HC-GLAD in short). To exploit node group connections, we construct
hypergraphs based on gold motifs and subsequently perform hypergraph
convolution. Furthermore, to preserve the hierarchy of real-world graphs, we
introduce hyperbolic geometry into this field and conduct both graph and
hypergraph embedding learning in hyperbolic space with hyperboloid model. To
the best of our knowledge, this is the first work to simultaneously apply
hypergraph with node group connections and hyperbolic geometry into this field.
Extensive experiments on several real world datasets of different fields
demonstrate the superiority of HC-GLAD on UGAD task. The code is available at
this https URL

无监督图层级异常检测方法中，本文提出了一种新颖的双双曲对比学习方法（HC-GLAD），通过构建基于 gold motifs 的超图，并在双曲空间中进行图和超图嵌入学习，实现了对节点组连接、超图以及真实世界图层次的保留，并在多个真实世界数据集上进行了广泛实验，证明了 HC-GLAD 在图层级异常检测任务中的优越性。