Graph anomaly detection (GAD) is a challenging binary classification problem
due to its different structural distribution between anomalies and normal nodes
-- abnormal nodes are a minority, therefore holding high heterophily and low
homophily compared to normal nodes. Furthermore, due to various time factors
and the annotation preferences of human experts, the heterophily and homophily
can change across training and testing data, which is called structural
distribution shift (SDS) in this paper. The mainstream methods are built on
graph neural networks (GNNs), benefiting the classification of normals from
aggregating homophilous neighbors, yet ignoring the SDS issue for anomalies and
suffering from poor generalization.
This work solves the problem from a feature view. We observe that the degree
of SDS varies between anomalies and normal nodes. Hence to address the issue,
the key lies in resisting high heterophily for anomalies meanwhile benefiting
the learning of normals from homophily. We tease out the anomaly features on
which we constrain to mitigate the effect of heterophilous neighbors and make
them invariant. We term our proposed framework as Graph Decomposition Network
(GDN). Extensive experiments are conducted on two benchmark datasets, and the
proposed framework achieves a remarkable performance boost in GAD, especially
in an SDS environment where anomalies have largely different structural
distribution across training and testing environments. Codes are open-sourced
in this https URL

针对图形异常检测中结构分布偏移的问题，本研究提出了一种名为图解构网络（GDN）的框架，通过限制异常特征的影响，同时从同质性中获益，来解决正常和异常节点之间的分类问题。在两个基准数据集上进行的广泛实验表明，该框架在异常检测中取得了显著的性能提升。