HC-GLAD：用于无监督图级异常检测的双曲对比学习
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
AI总结：
本文提出HC-GLAD方法，通过结合基于gold motifs的超图卷积与双曲空间嵌入学习，首次同时利用高阶节点组信息和双曲几何解决无监督图级异常检测问题，并在13个真实数据集上验证了其优越性。
摘要：
无监督图级异常检测（UGAD）近年来因其重要性而受到越来越多的关注。大多数现有方法依赖传统图神经网络（GNN），主要考虑一阶邻居之间的成对关系，这不足以捕获通常与异常相关的复杂高阶依赖。这一局限性凸显了在UGAD中探索高阶节点交互的必要性。此外，大多数先前工作忽略了现实世界图数据集中常见的底层性质（例如层次结构和幂律结构），而这些性质是UGAD任务中不可或缺的因素。在本文中，我们提出了一种新颖的双曲对比学习方法，用于无监督图级异常检测（简称HC-GLAD）。为了利用高阶节点组信息，我们基于预先设计的gold motifs构建超图，并随后执行超图卷积。此外，为了保留现实世界图的层次结构，我们将双曲几何引入该领域，并在双曲空间中采用双曲面模型同时进行图和超图嵌入学习。据我们所知，这是首个在该领域同时应用带有节点组信息的超图和双曲几何的工作。在13个不同领域的真实世界数据集上的大量实验证明了HC-GLAD在UGAD任务上的优越性。代码可在https://github.com/Yali-F/HC-GLAD获取。
英文摘要：
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mainly consider pairwise relationships between first-order neighbors, which is insufficient to capture the complex high-order dependencies often associated with anomalies. This limitation underscores the necessity of exploring high-order node interactions in UGAD. In addition, most previous works ignore the underlying properties (e.g., hierarchy and power-law structure) which are common in real-world graph datasets and therefore are indispensable factors in the 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 high-order node group information, we construct hypergraphs based on pre-designed 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 the hyperboloid model. To the best of our knowledge, this is the first work to simultaneously apply hypergraph with node group information and hyperbolic geometry in this field. Extensive experiments on 13 real-world datasets of different fields demonstrate the superiority of HC-GLAD on the UGAD task. The code is available at https://github.com/Yali-F/HC-GLAD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
