Graph anomaly detection (GAD) is a vital task since even a few anomalies can
pose huge threats to benign users. Recent semi-supervised GAD methods, which
can effectively leverage the available labels as prior knowledge, have achieved
superior performances than unsupervised methods. In practice, people usually
need to identify anomalies on new (sub)graphs to secure their business, but
they may lack labels to train an effective detection model. One natural idea is
to directly adopt a trained GAD model to the new (sub)graph for testing.
However, we find that existing semi-supervised GAD methods suffer from poor
generalization issue, i.e., well-trained models could not perform well on an
unseen area (i.e., not accessible in training) of the same graph. It may cause
great troubles. In this paper, we base on the phenomenon and propose a general
and novel research problem of generalized graph anomaly detection that aims to
effectively identify anomalies on both the training-domain graph and unseen
testing graph to eliminate potential dangers. Nevertheless, it is a challenging
task since only limited labels are available, and the normal background may
differ between training and testing data. Accordingly, we propose a data
augmentation method named \textit{AugAN} (\uline{Aug}mentation for
\uline{A}nomaly and \uline{N}ormal distributions) to enrich training data and
boost the generalizability of GAD models. Experiments verify the effectiveness
of our method in improving model generalizability.

本文提出了一种基于数据增强的半监督学习方法 AugAN，用于解决现有半监督图异常检测方法的泛化问题，并进一步提高模型的泛化能力，从而有效检测训练和测试数据中存在的异常。

通过数据增强提高图形异常检测模型的泛化能力

Improving Generalizability of Graph Anomaly Detection Models via Data  Augmentation

提出了用于解决图异常检测中普适性问题的数据增强方法，并验证了其提高 GAD 模型通用性的有效性。

通过数据增强提高图形异常检测模型的泛化性能

Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Graph Neural Networks (GNNs) are widely used for graph representation
learning. Despite its prevalence, GNN suffers from two drawbacks in the graph
classification task, the neglect of graph-level relationships, and the
generalization issue. Each graph is treated separately in GNN message
passing/graph pooling, and existing methods to address overfitting operate on
each individual graph. This makes the graph representations learnt less
effective in the downstream classification. In this paper, we propose a
Class-Aware Representation rEfinement (CARE) framework for the task of graph
classification. CARE computes simple yet powerful class representations and
injects them to steer the learning of graph representations towards better
class separability. CARE is a plug-and-play framework that is highly flexible
and able to incorporate arbitrary GNN backbones without significantly
increasing the computational cost. We also theoretically prove that CARE has a
better generalization upper bound than its GNN backbone through
Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 10
well-known GNN backbones on 9 benchmark datasets validate the superiority and
effectiveness of CARE over its GNN counterparts.

本文提出了一种 Class-Aware Representation refinement (CARE) 的方法，通过计算一个简单而强大的类别表示，并注入到图的表示中来提高类别可分离性，从而解决了图神经网络（GNN）在图分类任务中对于图层级关系和泛化问题的缺陷，并在多项实验中验证了其有效性。