Graph Anomaly Detection
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Benchmarks
Amazon-Fraud
Yelp-Fraud
Most implemented
Energy Transformer
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Papers
Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection
In recent years, graph anomaly detection (GAD) based on frequency-domain filtering have achieved promising results. However, existing approaches still face three major challenges: First, they use static basic function to…
Graph Anomaly DetectionOnline Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection
Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing methods often fail for two reasons. First, the…
Graph Anomaly DetectionTest-time AdaptationNode-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally fall into two paradigms. GNN-based metho…
Graph Anomaly DetectionFraud DetectionClue-Guided Money Laundering Group Discovery
Money Laundering Group Discovery (MLGD) aims to identify hidden criminal groups and recover their complete structures in large-scale financial networks. Existing graph anomaly detection methods mainly produce node-level …
Graph Anomaly DetectionTowards Anomaly Detection on Relational Data
Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-ex…
Graph Anomaly DetectionControlled Dynamics Attractor Transformer
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy …
Representation LearningGraph Anomaly DetectionGraph Classification