May 18, 2023 · We propose a novel meta-learning based framework, MetaGAD, that learns to adapt the knowledge from self-supervised learning to few-shot supervised learning for ...

May 18, 2023 · ABSTRACT. Graph anomaly detection has long been an important problem in various domains pertaining to information security such as finan-.

Aug 26, 2024 · This paper introduces a novel meta-learning-based framework called MetaGAD that addresses the problem of graph anomaly detection with limited ...

Arxiv 2023: MetaGAD: Learning to Meta-Transfer for Few-shot Graph Anomaly Detection [Paper]. Contrastive Learning. TNNLS 2021: Anomaly Detection on Attributed ...

MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection. X Xu, K Ding, C Chen, K Shu. arXiv preprint arXiv:2305.10668, 2023. 1, 2023. SST: Multi ...

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection ... Few-shot Network Anomaly Detection via Cross-network Meta-learning · 2 code ...

MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection. CoRR abs/2305.10668 (2023). [i35]. view. electronic edition via DOI (open access) ...

Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam, ...

May 25, 2023 · Bibliographic details on MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection.

In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.