Rethinking graph neural networks for anomaly detection

J Tang, J Li, Z Gao, J Li - International Conference on …, 2022 - proceedings.mlr.press
Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …
被引用次数：135 相关文章

Rethinking Graph Neural Networks for Anomaly Detection

J Tang, J Li, Z Gao, J Li - arXiv preprint arXiv:2205.15508, 2022 - arxiv.org
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of
the key components for GNN design is to select a tailored spectral filter, we take the first step …

Rethinking Graph Neural Networks for Anomaly Detection

J Tang, J Li, Z Gao, J Li - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …

Rethinking Graph Neural Networks for Anomaly Detection

J Tang, J Li, Z Gao, J Li - Proceedings of the International …, 2022 - repository.ust.hk
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of
the key components for GNN design is to select a tailored spectral filter, we take the first step …