BOURNE：用于统一图异常检测的自举自监督学习框架
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
AI总结：
提出统一图异常检测框架BOURNE，通过自举自监督学习提取节点与边上下文并交换嵌入实现相互检测，免除了负采样需求，在六个基准上验证了有效性与效率。
摘要：
图异常检测（GAD）近年来因其在社交网络、金融风险管理和交通分析等众多领域的关键应用而受到越来越多的关注。现有的GAD方法根据被检测图对象的类型，可分为节点和边异常检测模型。然而，这些方法通常将节点和边异常视为独立的任务，忽略了它们在真实图中的关联性和频繁共现性。因此，它们无法利用节点和边异常提供的互补信息进行相互检测。此外，最先进的GAD方法（如CoLA和SL-GAD）严重依赖对比学习中的负对采样，这带来了高昂的计算成本，阻碍了它们向大图的扩展性。为了解决这些局限性，我们提出了一种基于自举自监督学习的统一图异常检测新框架（命名为BOURNE）。我们提取以每个目标节点为中心的子图（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。这些视图使用图和超图神经网络进行编码，以捕获节点、边及其关联上下文的表示。通过在节点和边之间交换上下文嵌入并测量嵌入空间中的一致性，我们实现了节点和边异常的相互检测。此外，BOURNE消除了负采样的需求，从而提高了处理大图的效率。在六个基准数据集上进行的大量实验证明了BOURNE在检测节点和边异常方面具有卓越的有效性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
