Abstract: Graph anomaly detection aims to detect abnormal nodes from attribute networks,and is highly valued by researchers due to its profound practical significance in many application fields such as finance,electronic trade,and spam sender detection.Traditional non deep learning methods can only capture the shallow structure of the graph,and researchers have proposed anomaly detection models based on deep neural networks to address this issue.However,these models do not take into account the centrality differences of nodes in the graph,which can lead to information loss or introduce noise from remote nodes when capturing local information of nodes.In addition,they ignore the feature information in the attribute space,which can provide additional anomaly monitoring signals.Therefore,this paper proposes a novel graph anomaly detection framework PC-GAD(personalized PageRank and contrastive learning based graph anomaly detection) from an unsupervised perspective.Firstly,a dynamic sampling strategy is proposed,which calculates the personalized PageRank vector of each node in the graph to determine the corresponding size of subgraph samples,avoiding the loss of local information and noise introduction.Secondly,for each node,the abnormal supervision signals are captured from the perspective of topology and attribute space,and the corresponding contrastive learning objective is designed to comprehensively learn potential abnormal patterns.Finally,after multiple rounds of contrast and prediction,the degree of abnormality of each node is evaluated according to the score of the output outlier.To verify the effectiveness of the proposed model,a large number of comparative experiments are conducted with the benchmark models on six real datasets.Experimental results have verified that PC-GAD can comprehensively identify abnormal nodes in the graph,and the AUC value increases by 1.42% compared to existing models.

Key words: Graph anomaly detection, Personalized PageRank, Graph neural network, Graph contrastive learning