论文标题
重建增强的多视图对比度学习，用于归因网络的异常检测
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
论文作者
论文摘要
在许多实际应用中，例如财务欺诈检测和网络安全，从归因网络中检测异常节点非常重要。由于异常节点与其他对应物之间的复杂相互作用及其在属性方面的不一致之处，因此这项任务是具有挑战性的。本文提出了一个自我监督的学习框架，该框架共同优化了基于学习的多视图对比度模块和基于属性重建的模块，以更准确地检测属性网络的异常情况。具体而言，首先建立了两个对比的学习观点，这使该模型可以更好地编码与异常相关的丰富本地和全球信息。还引入了基于掩盖自动编码器的重建模块的属性一致性原理的动机，以识别具有较大重建错误的节点，然后被视为异常。最后，将两个互补模块集成在一起，以更准确地检测异常节点。在五个基准数据集上进行的广泛实验表明，我们的模型的表现优于当前最新模型。
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.
