Copertina

Generalist Graph Anomaly Detection via Prototype-Based Distillation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xu, Yiming, Chen, Zihan, Peng, Zhen, Wang, Song, Shi, Bin, Dong, Bo, Shen, Chao
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
di: Xu, Yiming, et al.
Pubblicazione: (2025)

Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection
di: Xu, Yiming, et al.
Pubblicazione: (2025)

Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
di: Xu, Yiming, et al.
Pubblicazione: (2025)

Out-of-Distribution Generalization on Graphs via Progressive Inference
di: Xu, Yiming, et al.
Pubblicazione: (2025)

Learning Dynamic Graph Representations through Timespan View Contrasts
di: Xu, Yiming, et al.
Pubblicazione: (2026)

DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
di: Zheng, Jialun, et al.
Pubblicazione: (2025)

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
di: Niu, Chaoxi, et al.
Pubblicazione: (2024)

NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity
di: Wei, Kaifeng, et al.
Pubblicazione: (2026)

TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
di: Xu, Yiming, et al.
Pubblicazione: (2026)

TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection
di: Zhang, Xiong, et al.
Pubblicazione: (2026)

EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts
di: Zhang, Kunyu, et al.
Pubblicazione: (2025)

From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
di: Liu, Yixin, et al.
Pubblicazione: (2026)

ARC: A Generalist Graph Anomaly Detector with In-Context Learning
di: Liu, Yixin, et al.
Pubblicazione: (2024)

Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
di: Zhao, Qiuran, et al.
Pubblicazione: (2026)

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
di: Liu, Yujing, et al.
Pubblicazione: (2026)

Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
di: Dong, Zhijin, et al.
Pubblicazione: (2024)

CLDG: Contrastive Learning on Dynamic Graphs
di: Xu, Yiming, et al.
Pubblicazione: (2024)

Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
di: Wang, Fuyun, et al.
Pubblicazione: (2026)

KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
di: Zhou, Andy, et al.
Pubblicazione: (2024)

RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
di: Xu, Charles, et al.
Pubblicazione: (2024)

Graph Distillation with Eigenbasis Matching
di: Liu, Yang, et al.
Pubblicazione: (2023)

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
di: Li, Pingzhi, et al.
Pubblicazione: (2025)

How to Use Graph Data in the Wild to Help Graph Anomaly Detection?
di: Cao, Yuxuan, et al.
Pubblicazione: (2025)

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
di: Wang, Yili, et al.
Pubblicazione: (2024)

GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
di: Zhang, Guibin, et al.
Pubblicazione: (2024)

Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
di: Dong, Xiangyu, et al.
Pubblicazione: (2023)

FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
di: Chen, Zihan, et al.
Pubblicazione: (2024)

Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations
di: Guo, Qi, et al.
Pubblicazione: (2024)

Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning
di: Wang, Zitong, et al.
Pubblicazione: (2024)

Generative Semi-supervised Graph Anomaly Detection
di: Qiao, Hezhe, et al.
Pubblicazione: (2024)

Active Hypothesis Testing for Correlated Combinatorial Anomaly Detection
di: Yang, Zichuan, et al.
Pubblicazione: (2026)

UMGAD: Unsupervised Multiplex Graph Anomaly Detection
di: Li, Xiang, et al.
Pubblicazione: (2024)

Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
di: Zhou, Yingjie, et al.
Pubblicazione: (2026)

Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
di: Bei, Yuanchen, et al.
Pubblicazione: (2024)

Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
di: Wo, Zengyi, et al.
Pubblicazione: (2025)

A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
di: Lei, Yiming, et al.
Pubblicazione: (2026)

Towards Cross-domain Few-shot Graph Anomaly Detection
di: Chen, Jiazhen, et al.
Pubblicazione: (2024)

Graph Evidential Learning for Anomaly Detection
di: Wei, Chunyu, et al.
Pubblicazione: (2025)

Interpreting What Typical Fault Signals Look Like via Prototype-matching
di: Chen, Qian, et al.
Pubblicazione: (2024)

GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
di: Yang, Yifei, et al.
Pubblicazione: (2023)