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A comprehensive survey on graph anomaly detection with deep learning
Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …
the others in the sample. Over the past few decades, research on anomaly mining has …
A survey on anomaly detection for technical systems using LSTM networks
Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …
decreased efficiency as well as partial or complete system failure. As the causes of …
Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
Deep learning for anomaly detection: A review
Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …
research area in various research communities for several decades. There are still some …
Pick and choose: a GNN-based imbalanced learning approach for fraud detection
Graph-based fraud detection approaches have escalated lots of attention recently due to the
abundant relational information of graph-structured data, which may be beneficial for the …
abundant relational information of graph-structured data, which may be beneficial for the …
[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …
installed in residential buildings. If leveraged properly, that data could assist end-users …
Addressing heterophily in graph anomaly detection: A perspective of graph spectrum
Graph anomaly detection (GAD) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …
that they are connected to vast normal nodes. The current solutions upon Graph Neural …