Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of link-level anomalies and graph representation learning for identifying categorically anomalous graph links. First, we introduce a fine-grained taxonomy for edge-level anomalies leveraging structural, temporal, and contextual graph properties. Based on these properties, we introduce a method for generating and injecting typed anomalies into graphs. Next, we introduce a novel method to generate continuous-time dynamic graphs featuring consistencies across either or combinations of time, structure, and context. To enable temporal graph learning methods to detect specific types of anomalous links rather than the bare existence of a link, 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. Comprehensive benchmarks on synthetic and real-world datasets -- featuring synthetic and labeled organic anomalies and employing six state-of-the-art link prediction methods -- validate our taxonomy and generation processes for anomalies and benign graphs, as well as our approach to adapting methods for anomaly detection. Our results 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但尚未在学习算法背景下得到充分探索的领域。本文率先对链接级异常进行了结构化分析，并利用图表示学习来识别类别异常的图链接。首先，我们利用图的结构、时间和上下文属性，为边级异常引入了一种细粒度的分类法。基于这些属性，我们提出了一种在图中生成并注入类型化异常的方法。接着，我们介绍了一种新颖的方法来生成在时间、结构和上下文或其组合上具有一致性的连续时间动态图。为了使时序图学习方法能够检测特定类型的异常链接，而非仅仅检测链接是否存在，我们通过以下方式扩展了通用的链接预测设置：(1) 将链接存在性条件化于上下文边属性；(2) 改进训练机制以适应负边采样器中多样的扰动。在合成和真实世界数据集上进行的综合基准测试——包含合成和标记的有机异常，并采用了六种最先进的链接预测方法——验证了我们针对异常和良性图的分类法、生成过程，以及我们为适应异常检测而调整方法的方法。我们的结果表明，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方面各有所长。最后，我们总结了一系列全面的发现，强调了未来研究的机会。