We propose a dual-bootstrapped self-supervised approach, namely DualGAD, that consists of one generative module and one contrastive module.
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. https://doi.org/10.1016/j.ins.2024.120520 ·. Journal: Information Sciences ...
This repository showcases a curated collection of research literature on imbalanced learning on graphs. We have categorized this literature according to the ...
Jul 28, 2023 · We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE).
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Hua Li. ·. Zhangli Hu. ·. [...] ·. Jiaping Liu · Share · DualGAD: Dual-Bootstrapped Self-Supervised Learning for Graph Anomaly Detection · Article. March 2024.
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection ... Graph anomaly detection (GAD) is an emerging and essential research ...
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. Authors. Hui Tang · ORCID ID · Xun Liang · Jun Wang · Sensen Zhang · ORCID ID ...
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5: 587. Journal article. DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. Request PDF. Restricted access. Information Sciences.
In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality ...
An radicals construction technique based on dual quaternions and hierarchical transformers · DualGAD: Dual-bootstrapped self-supervised learning for graph ...