Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning-based approaches. In this paper, we pioneer structured analyses of link-level anomalies and graph representation learning for identifying anomalous links in these graphs. First, we introduce a fine-grain taxonomy for edge-level anomalies leveraging structural, temporal, and contextual graph properties. We present a method for generating and injecting such typed anomalies into graphs. Next, we introduce a novel method to generate continuous-time dynamic graphs with consistent patterns across time, structure, and context. To allow temporal graph methods to learn the link anomaly detection task, we extend the generic link prediction setting by: (1) conditioning link existence on contextual edge attributes; and (2) refining the training regime to accommodate diverse perturbations in the negative edge sampler. Building on this, we benchmark methods for anomaly detection. Comprehensive experiments on synthetic and real-world datasets -- featuring synthetic and labeled organic anomalies and employing six state-of-the-art learning methods -- validate our taxonomy and generation processes for anomalies and benign graphs, as well as our approach to adapting link prediction methods for anomaly detection. Our results further reveal that different learning methods excel in capturing different aspects of graph normality and detecting different types of anomalies. We conclude with a comprehensive list of findings highlighting opportunities for future research.

翻译：连续时间动态图中的异常检测是一个新兴领域，但在基于学习的方法背景下尚未得到充分探索。本文首次对链接级异常进行了结构化分析，并利用图表示学习技术来识别此类图中的异常链接。首先，我们利用图的结构、时间和上下文属性，提出了一种细粒度的边级异常分类体系。我们提出了一种方法，用于生成此类类型化异常并将其注入图中。其次，我们引入了一种新颖的方法来生成在时间、结构和上下文上具有一致模式的连续时间动态图。为了使时序图方法能够学习链接异常检测任务，我们通过以下方式扩展了通用链接预测框架：(1) 将链接存在性条件建立在上下文边属性上；(2) 改进训练机制以适应负边采样器中的多样化扰动。在此基础上，我们对异常检测方法进行了基准测试。在合成和真实数据集上进行的综合实验——包含合成和标记的有机异常，并采用了六种最先进的学习方法——验证了我们提出的异常与良性图分类体系及生成过程，以及将链接预测方法适配于异常检测的途径。我们的结果进一步表明，不同的学习方法在捕捉图正常性的不同方面以及检测不同类型的异常上各有所长。最后，我们总结了一系列重要发现，为未来研究指明了方向。