ArticleSensors (Basel, Switzerland)2026
ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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