| The research of anomaly detection in attributed network aims to find the abnormal nodes which are different from most of the nodes in attribute and feature.However,most existing methods ignore the joint interaction between network structure and node attributed,and the network can only learn low quality node feature because of the network noise,which influences the effectiveness of anomaly detection algorithm.This dissertation proposes not only a deep joint representation learning framework but an anomaly detection algorithm based on the above framework.In order to capture the cross-model interaction between network structure,and node node attribute.This dissertation designed an Association Adversarial Regularization Joint Learning Model.The model uses two encoders to learn network structure feature embedding and node attribute feature embedding jointly in the hidden vector space,and regularizes the two feature representations by adversarial regularization module,then fuses the two features to realize the cross-model joint learning of network structure and network node attribute.Finally,a hyperspherical learning mechanism is introduced on the fusion features to detect anomalies by calculating the distance between feature fusion nodes and the center of the hypersphere in low-dimensional latent space.In order to solve the problem in which model has a descend in feature extraction performance due to the stacked of layers in the process of structural feature extraction,this dissertation has introduce the idea of non-smooth aggregation node feature extraction,which is introduced to alleviate the phenomenon of excessive smoothness between all layers of graph convolutional neural network.Based on AARJL,this dissertation proposed an Associative Adversarial Regularized Attributed Network Anomaly Detection（AARAN）.Feature aggregation methods is used to extract semantic information layer by layer in graph convolutional network,and by the means of semantic alignment achieve mapping semantic information and network structural features to the same neighborhood.In this dissertation,put AARAN compared with the latest methods on Cora,Citeseer and Pubmed data sets.The results showed that AARAN improved the AUC score by 4.62% and AP score by 5.02% compared with the traditional graph-based anomaly detection method.It is proved that AARAN can improve the ability of anomaly detection in the attributed network. |
