Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively. Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be detected by measuring the distance of nodes to the learned center of each hypersphere in the latent structure space and attribute space, respectively. Extensive experiments on the real-world attributed networks show that Dual-SVDAE consistently outperforms the state-of-the-arts, which demonstrates the effectiveness of the proposed method.

中文翻译：

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用于属性网络异常检测的深度双支持向量数据描述

网络在现实世界中无处不在，例如社交网络和通信网络，网络异常检测旨在找到结构或属性模式与大多数参考节点显着偏离的节点。然而，大多数传统的异常检测方法忽略了数据点之间的关系结构信息，因此不能有效地泛化到图结构数据上。在本文中，我们提出了一种基于深度双支持向量数据描述的自动编码器（Dual-SVDAE）的端到端模型，用于属性网络的异常检测，它同时考虑了属性网络的结构和属性。具体来说，Dual-SVDAE 由结构自编码器和属性自编码器组成，分别用于学习节点在结构空间和属性空间中的潜在表示。然后，对它们施加双超球面学习机制，分别从结构和属性角度学习正常节点的两个超球面。此外，为了实现网络结构和属性的联合学习，我们将结构嵌入和属性嵌入融合作为特征解码器的最终输入来生成节点属性。最后，可以通过分别在潜在结构空间和属性空间中测量节点到每个超球面学习中心的距离来检测异常节点。