Graph anomaly detection has attracted considerable attention in recent years.
This paper introduces a novel approach that leverages metapath-based
semi-supervised learning, addressing the limitations of previous methods. We
present a new framework, Metapath-based Semi-supervised Anomaly Detection
(MSAD), incorporating GCN layers in both the encoder and decoder to efficiently
propagate context information between abnormal and normal nodes. The design of
metapath-based context information and a specifically crafted anomaly community
enhance the process of learning differences in structures and attributes, both
globally and locally. Through a comprehensive set of experiments conducted on
seven real-world networks, this paper demonstrates the superiority of the MSAD
method compared to state-of-the-art techniques. The promising results of this
study pave the way for future investigations, focusing on the optimization and
analysis of metapath patterns to further enhance the effectiveness of anomaly
detection on attributed networks.

介绍了一种基于元路径的半监督学习方法的新颖方法，通过在编码器和解码器中利用 GCN 层来高效传播异常和正常节点之间的上下文信息，从而克服了先前方法的局限性，并通过在七个真实网络上进行的一系列实验，证明了该方法相对于最先进技术的优越性。这项研究的有希望的结果为未来的研究铺平了道路，重点是优化和分析元路径模式，进一步提高属性网络上的异常检测的有效性。