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.

翻译：属性网络上的异常检测旨在找出行为与大多数节点显著不同的节点。通常，网络数据包含实体间关系的信息，而异常通常体现在这些关系中。因此，如何全面建模网络中的复杂交互模式仍是一个主要关注点。可以观察到，网络中的异常违反同质性假设。然而，现有研究大多仅间接而非明确地考虑这一现象。此外，正常实体的节点表示易受异常节点引入的噪声关系干扰。为解决上述问题，我们提出了一种新颖的用于属性网络异常检测的对比学习框架——**SCALA**，旨在通过将稀疏化引入传统方法，提升网络嵌入质量，并提供一种量化每个节点异常分数的新测量方法。在五个基准真实世界数据集上进行的大量实验表明，SCALA在所有基线方法上始终显著超越其性能。