标题： 图异常检测中的双向课程学习：同质性和异质性的双重关注 显示英文标题

标题： Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity

摘要： 图异常检测（GAD）旨在识别图中显著不同于正常模式的节点。大多数先前的研究是基于模型驱动的，重点在于通过改进模型结构来增强检测效果。然而，这些方法通常对所有节点一视同仁，忽略了不同节点对训练的不同贡献。因此，我们引入了图课程学习作为一种简单有效的即插即用模块来优化GAD方法。现有的图课程学习主要关注图的同质性，并将具有高同质性的节点视为简单节点。实际上，GAD模型不仅可以处理图的同质性，还可以处理异质性，这导致现有方法的不适配。为了解决这个问题，我们提出了一种创新的双向课程学习策略（BCL），该策略分别将与邻近节点具有较高和较低相似度的节点视为关注同质性和关注异质性方向上的简单节点，并优先对其进行训练。广泛的实验表明，BCL可以快速集成到现有的检测过程中，并显著提升十种GAD异常检测模型在七个常用数据集上的性能。 显示英文摘要

摘要： Graph anomaly detection (GAD) aims to identify nodes from a graph that are significantly different from normal patterns. Most previous studies are model-driven, focusing on enhancing the detection effect by improving the model structure. However, these approaches often treat all nodes equally, neglecting the different contributions of various nodes to the training. Therefore, we introduce graph curriculum learning as a simple and effective plug-and-play module to optimize GAD methods. The existing graph curriculum learning mainly focuses on the homogeneity of graphs and treats nodes with high homogeneity as easy nodes. In fact, GAD models can handle not only graph homogeneity but also heterogeneity, which leads to the unsuitability of these existing methods. To address this problem, we propose an innovative Bi-directional Curriculum Learning strategy (BCL), which considers nodes with higher and lower similarity to neighbor nodes as simple nodes in the direction of focusing on homogeneity and focusing on heterogeneity, respectively, and prioritizes their training. Extensive experiments show that BCL can be quickly integrated into existing detection processes and significantly improves the performance of ten GAD anomaly detection models on seven commonly used datasets.