Papers Graph Anomaly Detection
“Graph Anomaly Detection” 태그가 달린 논문 138편 · 필터 해제
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 ClassificationA Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depen…
Graph Anomaly DetectionModeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectral energy distributions, they overlook those that result in decrease…
Graph Anomaly DetectionGraph LearningTemporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomal…
Graph Anomaly DetectionTest-time AdaptationGraph LearningDDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection
Graph anomaly detection (GAD) aims to identify nodes or substructures whose behavior or attributes deviate significantly from the overall pattern in graph-structured data, with critical applications in financial risk con…
Graph Anomaly DetectionDetect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection methods still face two key challenges: 1) fixe…
Graph Anomaly DetectionGeneralist Graph Anomaly Detection via Prototype-Based Distillation
Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs, has recently gained growing attention. H…
Graph Anomaly DetectionGraph Neural NetworkLearning Dynamic Graph Representations through Timespan View Contrasts
The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological properties within static graphs to create se…
Graph Anomaly DetectionContrastive LearningNode ClassificationRethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning heterogeneous features across different …
Graph Anomaly DetectionNeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity
Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain "one-for-all" detection, but existing GGAD methods predominantly rely on the neighbor consistency principle, falling into t…
Graph Anomaly DetectionDomain GeneralizationTERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-rich approaches typically integrate struct…
Graph Anomaly DetectionData AugmentationLearning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabe…
Graph Anomaly DetectionMulti-Task LearningGAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform governance. However, existing GAD benchmarks are often restricted to sma…
Graph Anomaly DetectionFraud DetectionNeuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper …
Graph Anomaly DetectionNK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection
Graph anomaly detection aims to identify irregular patterns in graph-structured data. Most unsupervised GNN-based methods rely on the homophily assumption that connected nodes share similar attributes. However, real-worl…
Graph Anomaly Detection