Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module.

翻译：动态图上的异常检测旨在识别行为显著偏离图中观测到的常态模式及其时序信息的实体。该领域因在金融、网络安全、社交网络等领域的应用而日益受到关注。然而，现有方法面临两大挑战：动态结构构建挑战——难以捕捉包含复杂时间信息的图结构，以及负采样挑战——无法为无监督学习构建高质量的负样本。为解决这些问题，我们提出了动态图上的无监督生成式异常检测方法（GADY）。针对第一个挑战，我们提出了一种连续动态图模型来捕获细粒度信息，突破了现有离散方法的局限性。具体而言，我们采用结合位置特征的消息传递框架获取边嵌入，并通过解码边嵌入识别异常。针对第二个挑战，我们首次引入生成对抗网络来生成负交互。此外，我们设计了一种损失函数，在保证生成样本多样性与质量的同时，调整生成器的训练目标。大量实验表明，我们提出的GADY在三个真实世界数据集上的表现显著优于此前的最优方法。补充实验进一步验证了模型设计的有效性及每个模块的必要性。