  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.
