  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.
