基于超图对比学习的异常事件检测
Abnormal Event Detection via Hypergraph Contrastive Learning
AI总结：
针对属性异构信息网络中的无监督异常事件检测问题，提出超图对比学习方法AEHCL，通过事件内和事件间对比模块捕获异常交互，显著提升了检测性能。
摘要：
异常事件检测旨在挖掘涉及实体间的异常交互，在许多实际应用中发挥着重要作用。以往的工作大多将此任务过度简化为检测异常的成对交互。然而，现实世界的事件可能包含多类型属性实体及其间的复杂交互，从而形成属性异构信息网络（AHIN）。随着社交网络的兴起，AHIN中的异常事件检测已成为一项重要但鲜有探索的任务。本文首先研究AHIN中的无监督异常事件检测问题。事件被视为AHIN的星型模式实例，并进一步通过超图进行建模。我们提出了一种名为AEHCL的新型超图对比学习方法，以充分捕获异常事件模式。AEHCL设计了事件内和事件间对比模块，以利用自监督的AHIN信息。事件内对比模块捕获事件内部的成对和多变量交互异常，事件间模块则捕获事件间的上下文异常。这两个模块相互协作，共同提升彼此的性能并改善检测结果。在测试阶段，进一步提出了一种基于对比学习的异常事件评分函数，用于衡量事件的异常程度。在三个不同场景数据集上的大量实验证明了AEHCL的有效性，其结果分别在平均精度（AP）和曲线下面积（AUC）上较最先进的基线方法提升了12.0%和4.6%。
英文摘要：
Abnormal event detection, which refers to mining unusual interactions among involved entities, plays an important role in many real applications. Previous works mostly over-simplify this task as detecting abnormal pair-wise interactions. However, real-world events may contain multi-typed attributed entities and complex interactions among them, which forms an Attributed Heterogeneous Information Network (AHIN). With the boom of social networks, abnormal event detection in AHIN has become an important, but seldom explored task. In this paper, we firstly study the unsupervised abnormal event detection problem in AHIN. The events are considered as star-schema instances of AHIN and are further modeled by hypergraphs. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns. AEHCL designs the intra-event and inter-event contrastive modules to exploit self-supervised AHIN information. The intra-event contrastive module captures the pair-wise and multivariate interaction anomalies within an event, and the inter-event module captures the contextual anomalies among events. These two modules collaboratively boost the performance of each other and improve the detection results. During the testing phase, a contrastive learning-based abnormal event score function is further proposed to measure the abnormality degree of events. Extensive experiments on three datasets in different scenarios demonstrate the effectiveness of AEHCL, and the results improve state-of-the-art baselines up to 12.0% in Average Precision (AP) and 4.6% in Area Under Curve (AUC) respectively.
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。
