  Anomaly detection on attributed networks aims to find the nodes whose
behaviors are significantly different from other majority nodes. Generally,
network data contains information about relationships between entities, and the
anomaly is usually embodied in these relationships. Therefore, how to
comprehensively model complex interaction patterns in networks is still a major
focus. It can be observed that anomalies in networks violate the homophily
assumption. However, most existing studies only considered this phenomenon
obliquely rather than explicitly. Besides, the node representation of normal
entities can be perturbed easily by the noise relationships introduced by
anomalous nodes. To address the above issues, we present a novel contrastive
learning framework for anomaly detection on attributed networks,
\textbf{SCALA}, aiming to improve the embedding quality of the network and
provide a new measurement of qualifying the anomaly score for each node by
introducing sparsification into the conventional method. Extensive experiments
are conducted on five benchmark real-world datasets and the results show that
SCALA consistently outperforms all baseline methods significantly.
