.png){kind=icon}
.png){kind=icon}
Return
ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers
.png){kind=icon}
OA
AI
DOI：10.3390/s26103176.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Attributed graphs have recently emerged as a powerful tool for representing diverse data in numerous real-world sensors. Among various applications, unsupervised graph anomaly detection (UGAD) aims to identify abnormal data that significantly deviate from the majority of normal nodes without label annotations. Hence, UGAD can provide crucial assistance in enhancing the reliability of IoT, intelligent sensors and so on. Under the class-imbalanced reality caused by anomaly scarcity, the common paradigm of UGAD focuses on learning a model that primarily captures normal patterns. However, the traditional Graph Neural Network (GNN) paradigm suffers from local-aggregation limitations and over-smoothing, constraining their discrimination capacity. To address these issues, we introduce Graph Transformers (GTs) into UGAD task, termed as unsupervised attributed graph Anomaly detectioN wIth Masked grAph TransformErs (ANIMATE). Leveraging the global receptive field of Transformers, we can capture graph information that preserves the distinguishable characteristics of abnormalities from a global perspective. Furthermore, we employ masked auto-encoders to reconstruct node features and prompt our model to focus more on learning normal patterns. Additionally, we enhance the performance through a self-paced enhancement scheme specifically for UGAD tasks. Experiments conducted on various real-world benchmark datasets with organic anomalies validate the effectiveness of our proposed method compared to state-of-the-art competitors.
Keywords:
graph anomaly detection
unsupervised graph representation learning
masked autoencoder
transformers
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.1W
Citations:
20.9W