论文标题
在图形异常检测中提高栏
Raising the Bar in Graph-level Anomaly Detection
论文作者
论文摘要
图形异常检测已成为不同领域的关键主题，例如财务欺诈检测和检测社交网络中的异常活动。尽管大多数研究都集中在可视数据（例如图像）上的异常检测上，但在该检测中获得了高检测精度，但现有的图形深度学习方法目前表现出较差的性能。本文提高了图级异常检测的条形图，即在一组图中检测异常图的任务。通过利用自我监督的学习和转型学习中的思想，我们提出了一种新的深度学习方法，该方法通过解决了他们的一些已知问题，包括Hypersphere Collapse和Exprormance Flip，从而显着改善了现有的深层单级方法。涉及九种技术的九个现实世界数据集的实验表明，与最佳现有方法相比，我们的方法的平均性能提高了11.8％的AUC。
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most research has focused on anomaly detection for visual data such as images, where high detection accuracies have been obtained, existing deep learning approaches for graphs currently show considerably worse performance. This paper raises the bar on graph-level anomaly detection, i.e., the task of detecting abnormal graphs in a set of graphs. By drawing on ideas from self-supervised learning and transformation learning, we present a new deep learning approach that significantly improves existing deep one-class approaches by fixing some of their known problems, including hypersphere collapse and performance flip. Experiments on nine real-world data sets involving nine techniques reveal that our method achieves an average performance improvement of 11.8% AUC compared to the best existing approach.
