The deep fusion of topological structure and attribute information for anomaly detection in attributed networks

Attribute network anomaly detection has attracted more and more research attention due to its wide application in social media, financial transactions, and network security. However, most of the existing methods only consider the network structure or attribute information to detect anomalies, ignoring the combined information of the node structure and attributes in the network. A novel anomaly detection method in attributed networks based on walking autoencoder named RW2AEAD is proposed in this paper, considering structure and attribute information. Besides capturing the network's structural information by random walking, it gets the combined information of structures and the attributes that are closely related to the structures. And then, the structure and combined reconstruction error of node are obtained by inputting into the autoencoder composed of SkipGram and CBOW. In addition, the global attribute reconstruction error of the node is obtained through the multi-layer attribute autoencoder. Finally, the anomaly score of the node comprehensively considers the above three reconstruction errors, and detects anomalous nodes by setting the threshold and the score ranking. Experiments show that the performance of the proposed RW2AEAD is better than other baseline algorithms in four real datasets.