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。

HC-GLAD：双超边对比学习用于无监督图层次异常检测

HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised  Graph-Level Anomaly Detection

Graph-level anomaly detection (GAD) describes the problem of detecting graphs
that are abnormal in their structure and/or the features of their nodes, as
compared to other graphs. One of the challenges in GAD is to devise graph
representations that enable the detection of both locally- and
globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained
(node-level) or holistic (graph-level) properties, respectively. To tackle this
challenge we introduce a novel deep anomaly detection approach for GAD that
learns rich global and local normal pattern information by joint random
distillation of graph and node representations. The random distillation is
achieved by training one GNN to predict another GNN with randomly initialized
network weights. Extensive experiments on 16 real-world graph datasets from
diverse domains show that our model significantly outperforms seven
state-of-the-art models. Code and datasets are available at
this https URL

介绍了一种新的基于深度学习的图异常检测方法，该方法使用 GNN 来预测具有随机初始化网络权重的另一个 GNN，可以有效检测局部和全局异常的真实世界图数据集。

全局知识蒸馏的深度图层异常检测

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.

本文提出了一种基于 Graph Neural Networks 和 graph few-shot learning 算法的半监督节点分类方法，通过传递先前学习的辅助图中的结构知识来改进目标图上的分类精度，并在四个真实世界图数据集上的实验和消融研究中证明了该模型的有效性。