Anomaly detection on the attributed network has recently received increasing attention in many research fields, such as cybernetic anomaly detection and financial fraud detection. With the wide application of deep learning on graph representations, existing approaches choose to apply euclidean graph encoders as their backbone, which may lose important hierarchical information, especially in complex networks. To tackle this problem, we propose an efficient anomaly detection framework using hyperbolic self-supervised contrastive learning. Specifically, we first conduct the data augmentation by performing subgraph sampling. Then we utilize the hierarchical information in hyperbolic space through exponential mapping and logarithmic mapping and obtain the anomaly score by subtracting scores of the positive pairs from the negative pairs via a discriminating process. Finally, extensive experiments on four real-world datasets demonstrate that our approach performs superior over representative baseline approaches.
翻译:最近在许多研究领域,例如网络异常现象检测和金融欺诈检测,都日益受到越来越多的关注。随着在图表显示上广泛应用深层学习,现有方法选择使用电子clidean图形编码器作为其主干线,这可能会失去重要的等级信息,特别是在复杂的网络中。为了解决这一问题,我们建议使用双曲自我监督的对比性学习来建立一个高效的异常检测框架。具体地说,我们首先通过进行子谱取样来进行数据增强。然后,我们通过指数绘图和对数绘图来利用超双曲空间的等级信息,并通过歧视过程从负对子中减去正对子的分数,从而获得异常分数。最后,关于四个真实世界数据集的广泛实验表明,我们的方法优于有代表性的基线方法。