Graph structure patterns are widely used to model different area data
recently. How to detect anomalous graph information on these graph data has
become a popular research problem. The objective of this research is centered
on the particular issue that how to detect abnormal graphs within a graph set.
The previous works have observed that abnormal graphs mainly show node-level
and graph-level anomalies, but these methods equally treat two anomaly forms
above in the evaluation of abnormal graphs, which is contrary to the fact that
different types of abnormal graph data have different degrees in terms of
node-level and graph-level anomalies. Furthermore, abnormal graphs that have
subtle differences from normal graphs are easily escaped detection by the
existing methods. Thus, we propose a multi-representations space separation
based graph-level anomaly-aware detection framework in this paper. To consider
the different importance of node-level and graph-level anomalies, we design an
anomaly-aware module to learn the specific weight between them in the abnormal
graph evaluation process. In addition, we learn strictly separate normal and
abnormal graph representation spaces by four types of weighted graph
representations against each other including anchor normal graphs, anchor
abnormal graphs, training normal graphs, and training abnormal graphs. Based on
the distance error between the graph representations of the test graph and both
normal and abnormal graph representation spaces, we can accurately determine
whether the test graph is anomalous. Our approach has been extensively
evaluated against baseline methods using ten public graph datasets, and the
results demonstrate its effectiveness.

基于多重表示空间分离的图级异常感知检测框架，通过考虑节点级和图级异常的不同重要性，设计了异常感知模块来学习它们之间的特定权重，并通过四种加权图表示（正常锚点图、异常锚点图、训练正常图、训练异常图）严格分隔正常和异常图像空间，根据测试图像与正常和异常图像空间的图像表示之间的距离误差，准确判断测试图像是否异常。在十个公共图形数据集上广泛评估了我们的方法，结果表明其有效性。