Dynamic graph anomaly detection (DGAD) is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervised or semi-supervised: unsupervised methods avoid the need for labeled anomalies but often produce ambiguous boundary, whereas semi-supervised methods can overfit to the limited labeled anomalies and generalize poorly to unseen anomalies. To address this gap, we consider a largely underexplored problem in DGAD: learning a discriminative boundary from normal/unlabeled data, while leveraging limited labeled anomalies \textbf{when available} without sacrificing generalization to unseen anomalies. To this end, we propose an effective, generalizable, and model-agnostic framework with three main components: (i) residual representation encoding that capture deviations between current interactions and their historical context, providing anomaly-relevant signals; (ii) a restriction loss that constrain the normal representations within an interval bounded by two co-centered hyperspheres, ensuring consistent scales while keeping anomalies separable; (iii) a bi-boundary optimization strategy that learns a discriminative and robust boundary using the normal log-likelihood distribution modeled by a normalizing flow. Extensive experiments demonstrate the superiority of our framework across diverse evaluation settings.

翻译：动态图异常检测（DGAD）在许多实际应用中至关重要，但由于标记异常的稀缺性，该任务仍具挑战性。现有方法要么是无监督的，要么是半监督的：无监督方法避免了标记异常的需求，但往往产生模糊的边界；而半监督方法可能对有限的标记异常过拟合，并对未见异常泛化能力差。为弥补这一不足，我们考虑DGAD中一个尚未充分探索的问题：从正常/未标记数据中学习判别性边界，同时**在可用时**利用有限的标记异常，且不牺牲对未见异常的泛化能力。为此，我们提出一个有效、可泛化且模型无关的框架，包含三个主要组件：（i）残差表示编码，捕捉当前交互与其历史上下文之间的偏差，提供异常相关信号；（ii）限制损失，将正常表示约束在以两个同中心超球面为界的区间内，确保尺度一致性的同时保持异常的可分离性；（iii）双边界优化策略，利用归一化流建模的正常对数似然分布，学习判别性强且鲁棒的边界。大量实验证明了我们的框架在多种评估设置下的优越性。