  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.
