  Detecting abnormal nodes from attributed networks is of great importance in
many real applications, such as financial fraud detection and cyber security.
This task is challenging due to both the complex interactions between the
anomalous nodes with other counterparts and their inconsistency in terms of
attributes. This paper proposes a self-supervised learning framework that
jointly optimizes a multi-view contrastive learning-based module and an
attribute reconstruction-based module to more accurately detect anomalies on
attributed networks. Specifically, two contrastive learning views are firstly
established, which allow the model to better encode rich local and global
information related to the abnormality. Motivated by the attribute consistency
principle between neighboring nodes, a masked autoencoder-based reconstruction
module is also introduced to identify the nodes which have large reconstruction
errors, then are regarded as anomalies. Finally, the two complementary modules
are integrated for more accurately detecting the anomalous nodes. Extensive
experiments conducted on five benchmark datasets show our model outperforms
current state-of-the-art models.

Machine Learning