Gadbench: Revisiting and benchmarking supervised graph anomaly detection
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
[HTML][HTML] Neighborhood representative for improving outlier detectors
Over the decades, traditional outlier detectors have ignored the group-level factor when
calculating outlier scores for objects in data by evaluating only the object-level factor, failing …
calculating outlier scores for objects in data by evaluating only the object-level factor, failing …
FOOR: Be Careful for Outlier-Score Outliers When Using Unsupervised Outlier Ensembles
J Yang, S Rahardja, S Rahardja - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Outlier detection is a very important tool in analyzing patterns and detecting unexpected
events in social systems. However, the process of outlier detection could be fraught with …
events in social systems. However, the process of outlier detection could be fraught with …
Distributed Fault Diagnosis for Heterogeneous Multi-Agent Systems: A Hybrid Knowledge-Based and Data-Driven Method
Heterogeneous Multi-Agents System (MAS) has been attracting increasing attention in many
application areas, but the safety and reliability of MAS are still challenging issues. Fault …
application areas, but the safety and reliability of MAS are still challenging issues. Fault …