属性网络中基于变分图自编码器的异常节点检测方法

Anomaly Node Detection Method Based on Variational Graph Auto-Encoders in Attribute Networks

图神经网络为属性网络数据挖掘提供融合利用结构信息和属性信息的方法,但是在现阶段基于图自动编码器进行无监督属性网络异常节点检测时,常将正常节点子属性插值形成的节点误识别为异常节点,导致方法的假负率较高.针对上述问题,文中提出基于变分图自编码器的异常节点检测方法.模型包含两个编码器和一个解码器,利用一个编码器和一个解码器构成的变分自编码器模型,重建原始输入数据,再利用解码器和第二个编码器,使模型学习到不包含异常节点数据的网络隐层表达.通过双变分自编码器学习正常节点子特征,并利用重建误差作为节点的异常度量,将由正常节点子特征构成的正常节点判别为正常节点.在真实网络数据集上的实验表明,文中方法能有效进行属性网络异常节点检测.

Graph neural network provides a method of combining structural information and attribute information for attribute network data mining.However,the current unsupervised attribute network anomaly node detection based on graph auto-encoder cannot capture the sub-features of normal nodes,and the false negative rate is high.Anomaly node detection method based on variational graph auto-encoders is proposed to detect abnormal nodes in attribute networks,containing two encoders and a decoder.A variational auto-encoder model consisting of an encoder and a decoder is designed to reconstruct the original input data.A decoder and the second encoder are utilized to learn the latent representation of the network without abnormal node data.The features of normal nodes are learned by the dual variational auto-encoder,and the reconstruction error is applied as the anomaly measure of nodes.Normal nodes composed of normal node features are identified as normal nodes by taking reconstruction error as the anomaly measurement of nodes.Experiments on real network datasets show that the proposed method is able to detect abnormal nodes in attribute networks effectively.