  Graph anomaly detection has attracted considerable attention from various
domain ranging from network security to finance in recent years. Due to the
fact that labeling is very costly, existing methods are predominately developed
in an unsupervised manner. However, the detected anomalies may be found out
uninteresting instances due to the absence of prior knowledge regarding the
anomalies looking for. This issue may be solved by using few labeled anomalies
as prior knowledge. In real-world scenarios, we can easily obtain few labeled
anomalies. Efficiently leveraging labelled anomalies as prior knowledge is
crucial for graph anomaly detection; however, this process remains challenging
due to the inherently limited number of anomalies available. To address the
problem, we propose a novel approach that leverages metapath to embed actual
connectivity patterns between anomalous and normal nodes. To further
efficiently exploit context information from metapath-based anomaly subgraph,
we present a new framework, Metapath-based Graph Anomaly Detection (MGAD),
incorporating GCN layers in both the dual-encoders and decoders to efficiently
propagate context information between abnormal and normal nodes. Specifically,
MGAD employs GNN-based graph autoencoder as its backbone network. Moreover,
dual encoders capture the complex interactions and metapath-based context
information between labeled and unlabeled nodes both globally and locally.
Through a comprehensive set of experiments conducted on seven real-world
networks, this paper demonstrates the superiority of the MGAD method compared
to state-of-the-art techniques. The code is available at
https://github.com/missinghwan/MGAD.
