论文标题
Anomalydae：用于归因网络异常检测的双自动编码器
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
论文作者
论文摘要
归因网络上的异常检测旨在寻找节点，这些节点与大多数参考节点显着偏离，这在许多应用中都普遍存在，例如网络入侵检测和社交垃圾邮件发送者检测。但是，大多数现有方法忽略了网络结构和节点属性之间复杂的跨模式相互作用。在本文中，我们通过双自动编码器（Anomalydae）提出了一个深入的联合表示学习框架，以捕获高质量嵌入的网络结构和节点属性之间的复杂相互作用。具体而言，Anomalydae由结构自动编码器和属性自动编码器组成，以学习嵌入节点嵌入和属性在潜在空间中共同嵌入。此外，在结构编码器中采用了注意机制来学习一个节点与其邻居之间的重要性，以有效地捕获结构模式，这对异常检测很重要。此外，通过将节点嵌入和属性嵌入作为属性解码器的输入，可以在节点属性重建过程中学习网络结构和节点属性之间的交叉模式相互作用。最后，可以通过从结构和属性角度测量节点的重建误差来检测异常。对现实世界数据集的广泛实验证明了该方法的有效性。
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, AnomalyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.
