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.

属性网络异常检测由于其在社交媒体，金融交易和网络安全中的广泛应用而吸引了越来越多的研究关注。然而，大多数现有方法仅考虑网络结构或属性信息来检测异常，而忽略了网络中节点结构和属性的组合信息。考虑结构和属性信息，提出一种基于行走自动编码器RW2AEAD的属性网络异常检测方法。除了通过随机游走捕获网络的结构信息之外，它还获得结构的组合信息以及与结构密切相关的属性。然后，通过输入由SkipGram和CBOW组成的自动编码器，可以获得节点的结构和组合重建误差。另外，节点的全局属性重构错误是通过多层属性自动编码器获得的。最后，节点的异常分数综合考虑了上述三个重构错误，并通过设置阈值和分数排名来检测异常节点。实验表明，在四个真实数据集中，所提出的RW2AEAD的性能优于其他基线算法。并通过设置阈值和分数排名来检测异常节点。实验表明，在四个真实数据集中，所提出的RW2AEAD的性能优于其他基线算法。并通过设置阈值和分数排名来检测异常节点。实验表明，在四个真实数据集中，所提出的RW2AEAD的性能优于其他基线算法。