We propose a dual-bootstrapped self-supervised approach, namely DualGAD, that consists of one generative module and one contrastive module.
Graph anomaly detection (GAD) is an emerging and essential research field for discovering anomalous individuals (e.g., nodes or edges) that deviate ...
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. https://doi.org/10.1016/j.ins.2024.120520 ·. Journal: Information Sciences ...
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. Published:2024-05 Issue: Volume:668 Page:120520. ISSN:0020-0255. Container ...
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).
Missing: DualGAD: | Show results with:DualGAD:
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 ...
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DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection ... Graph anomaly detection (GAD) is an emerging and essential research ...
5: 587. Journal article. DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection. Request PDF. Restricted access. Information Sciences.
Nov 19, 2024 · DualGAD: Dual- bootstrapped self-supervised learning for graph anomaly detection. Information. Sciences 668 (2024), 120520. [38] Jianheng ...