从无监督到少样本图异常检测：一种多尺度对比学习方法
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
AI总结：
本文提出多尺度对比学习框架ANEMONE及其少样本扩展ANEMONE-FS，通过融合图的多尺度视图信息提升异常检测性能，在六个基准数据集上优于现有方法。
摘要：
从图数据中进行异常检测是许多应用中的一项重要数据挖掘任务，例如社交网络、金融和电子商务。现有的图异常检测工作通常只考虑单一尺度（视图）中的信息，因此不可避免地限制了它们在复杂图数据中捕捉异常模式的能力。为了解决这一局限性，我们提出了一种新颖的框架，即基于多尺度对比学习的图异常检测框架（简称ANEMONE）。通过使用图神经网络作为骨干网络来编码来自多个图尺度（视图）的信息，我们为图中的节点学习更好的表示。在同时最大化块级别和上下文级别实例之间的一致性的过程中，我们使用统计异常估计器根据多个视角的一致性程度来估计每个节点的异常分数。为了进一步利用现实应用中可能收集到的少量真实异常（少样本异常），我们进一步提出了一种扩展算法ANEMONE-FS，以将有价值的信息整合到我们的方法中。我们在纯无监督设置和少样本异常检测设置下进行了大量实验，并证明了所提出的方法ANEMONE及其变体ANEMONE-FS在六个基准数据集上一致地优于最先进的算法。
英文摘要：
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous patterns in complex graph data. To address this limitation, we propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short). By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph. In maximizing the agreements between instances at both the patch and context levels concurrently, we estimate the anomaly score of each node with a statistical anomaly estimator according to the degree of agreement from multiple perspectives. To further exploit a handful of ground-truth anomalies (few-shot anomalies) that may be collected in real-life applications, we further propose an extended algorithm, ANEMONE-FS, to integrate valuable information in our method. We conduct extensive experiments under purely unsupervised settings and few-shot anomaly detection settings, and we demonstrate that the proposed method ANEMONE and its variant ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark datasets.
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。
