  Anomaly detection on attributed networks attracts considerable research
interests due to wide applications of attributed networks in modeling a wide
range of complex systems. Recently, the deep learning-based anomaly detection
methods have shown promising results over shallow approaches, especially on
networks with high-dimensional attributes and complex structures. However,
existing approaches, which employ graph autoencoder as their backbone, do not
fully exploit the rich information of the network, resulting in suboptimal
performance. Furthermore, these methods do not directly target anomaly
detection in their learning objective and fail to scale to large networks due
to the full graph training mechanism. To overcome these limitations, in this
paper, we present a novel contrastive self-supervised learning framework for
anomaly detection on attributed networks. Our framework fully exploits the
local information from network data by sampling a novel type of contrastive
instance pair, which can capture the relationship between each node and its
neighboring substructure in an unsupervised way. Meanwhile, a well-designed
graph neural network-based contrastive learning model is proposed to learn
informative embedding from high-dimensional attributes and local structure and
measure the agreement of each instance pairs with its outputted scores. The
multi-round predicted scores by the contrastive learning model are further used
to evaluate the abnormality of each node with statistical estimation. In this
way, the learning model is trained by a specific anomaly detection-aware
target. Furthermore, since the input of the graph neural network module is
batches of instance pairs instead of the full network, our framework can adapt
to large networks flexibly. Experimental results show that our proposed
framework outperforms the state-of-the-art baseline methods on all seven
benchmark datasets.
