Graph-level anomaly detection aims to identify anomalous graphs from a
collection of graphs in an unsupervised manner. A common assumption of anomaly
detection is that a reasonable decision boundary has a hypersphere shape, but
may appear some non-conforming phenomena in high dimensions. Towards this end,
we firstly propose a novel deep graph-level anomaly detection model, which
learns the graph representation with maximum mutual information between
substructure and global structure features while exploring a hypersphere
anomaly decision boundary. The idea is to ensure the training data distribution
consistent with the decision hypersphere via an orthogonal projection layer.
Moreover, we further perform the bi-hypersphere compression to emphasize the
discrimination of anomalous graphs from normal graphs. Note that our method is
not confined to graph data and is applicable to anomaly detection of other data
such as images. The numerical and visualization results on benchmark datasets
demonstrate the effectiveness and superiority of our methods in comparison to
many baselines and state-of-the-arts.

本文提出了一种新的深度图形异常检测模型，该模型利用亚球形决策边界来通过最大互信息筛选图像特征，进而实现对无监督的异常图形识别