Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies.

翻译：图异常检测（GAD）因在社会网络、金融风险管理、交通分析等多个领域的广泛应用而日益受到关注。现有GAD方法可根据检测的图对象类型分为节点异常检测模型和边异常检测模型。然而，这些方法通常将节点异常和边异常视为独立任务，忽略了真实世界图中两者间的关联性与频繁共现性，因此未能利用节点异常与边异常提供的互补信息实现相互检测。此外，当前最先进的GAD方法（如CoLA和SL-GAD）严重依赖对比学习中的负样本对采样，导致计算成本高昂，难以扩展至大规模图。为解决上述局限，我们提出一种基于自举式自监督学习的统一图异常检测框架（命名为BOURNE）。我们以每个目标节点为中心提取子图（图视图）作为节点上下文，并将其转化为对偶超图（超图视图）作为边上下文。通过图神经网络与超图神经网络对这些视图进行编码，捕获节点、边及其相关上下文的表示。通过交换节点与边的上下文嵌入，并度量嵌入空间中的一致性，实现节点异常与边异常的相互检测。此外，我们采用自举式训练策略，无需负样本采样，使BOURNE能够高效处理大规模图。在六个基准数据集上的广泛实验表明，BOURNE在检测节点和边异常方面具有优越的有效性与效率。