Abnormal Event Detection via Hypergraph Contrastive Learning

TL;DR

This paper introduces AEHCL, a hypergraph contrastive learning approach for unsupervised abnormal event detection in attributed heterogeneous information networks, effectively capturing complex interactions and outperforming existing methods.

Contribution

The paper proposes a novel hypergraph contrastive learning method, AEHCL, for unsupervised abnormal event detection in AHIN, addressing complex multi-typed entity interactions.

Findings

AEHCL outperforms baselines with up to 12.0% AP improvement.

The intra- and inter-event modules effectively capture anomalies.

Extensive experiments validate the method's robustness across datasets.

Abstract

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.