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Graph Anomaly Detection

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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection

Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective

Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening

EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection

Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection

CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection

Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning

G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition

Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

Out-of-Distribution Detection on Graphs: A Survey

CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection

Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity

Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection

UMGAD: Unsupervised Multiplex Graph Anomaly Detection

UniGAD: Unifying Multi-level Graph Anomaly Detection

High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach

ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly Detection

Graph Pre-Training Models Are Strong Anomaly Detectors

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

Towards Cross-domain Few-shot Graph Anomaly Detection

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations

PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities

Deep Graph Anomaly Detection: A Survey and New Perspectives

Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering

A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs

Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

Graph Anomaly Detection with Noisy Labels by Reinforcement Learning

Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement

ARC: A Generalist Graph Anomaly Detector with In-Context Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.