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, BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies.

翻译：图异常检测近年来因其在社会网络、金融风险管理及交通分析等关键领域的应用而备受关注。现有图异常检测方法根据检测对象类型可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与边异常视为独立任务，忽视了真实图中两者之间的关联性与高频共现模式，因此未能利用节点与边异常提供的互补信息实现相互检测。此外，当前最优的图异常检测方法（如CoLA和SL-GAD）在对比学习中高度依赖负样本对采样，导致计算成本高昂，难以扩展至大规模图。为解决上述局限，本文提出一种基于自举自监督学习的新型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，并将其转换为对偶超图（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上述视图进行编码，捕获节点、边及其关联上下文的表征。通过交换节点与边之间的上下文嵌入并度量嵌入空间中的一致性，实现了节点异常与边异常的相互检测。此外，BOURNE可消除负采样需求，从而提升其对大规模图的处理效率。在六个基准数据集上的广泛实验表明，BOURNE在检测节点异常与边异常方面均具有卓越的有效性与高效性。