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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 …
Class-imbalanced learning on graphs: A survey
Rapid advancement in machine learning is increasing the demand for effective graph data
analysis. However, real-world graph data often exhibits class imbalance, leading to poor …
analysis. However, real-world graph data often exhibits class imbalance, leading to poor …
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 …
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 …
Bond: Benchmarking unsupervised outlier node detection on static attributed graphs
Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …
numerous applications. Despite the proliferation of algorithms developed in recent years for …
Dgraph: A large-scale financial dataset for graph anomaly detection
Abstract Graph Anomaly Detection (GAD) has recently become a hot research spot due to its
practicability and theoretical value. Since GAD emphasizes the application and the rarity of …
practicability and theoretical value. Since GAD emphasizes the application and the rarity of …
Alleviating structural distribution shift in graph anomaly detection
Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …
different structural distribution between anomalies and normal nodes---abnormal nodes are …
Auc-oriented graph neural network for fraud detection
Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they
suffer from imbalanced labels due to limited fraud compared to the overall userbase. This …
suffer from imbalanced labels due to limited fraud compared to the overall userbase. This …
Comga: Community-aware attributed graph anomaly detection
Graph anomaly detection, here, aims to find rare patterns that are significantly different from
other nodes. Attributed graphs containing complex structure and attribute information are …
other nodes. Attributed graphs containing complex structure and attribute information are …
Learning transactional behavioral representations for credit card fraud detection
Credit card fraud detection is a challenging task since fraudulent actions are hidden in
massive legitimate behaviors. This work aims to learn a new representation for each …
massive legitimate behaviors. This work aims to learn a new representation for each …