Abstract
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection. To address these challenges, we present a Diffusion-based Graph Anomaly Detector (DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance its proficiency by guiding it with discriminative content. This innovative approach leverages diffusion sampling to infuse the latent space with discriminative content and introduces a content-preservation mechanism that retains valuable information across different scales, significantly improving its adeptness at identifying anomalies with limited time and space complexity. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets with various metrics, demonstrated its exceptional performance.
Citation:
@article{DiffGAD,
author = {Jinghan Li and
Yuan Gao and
Jinda Lu and
Junfeng Fang and
Congcong Wen and
Hui Lin and
Xiang Wang},
title = {DiffGAD: {A} Diffusion-based Unsupervised Graph Anomaly Detector},
journal = {CoRR},
volume = {abs/2410.06549},
year = {2024}
}
Jinghan Li
李静涵
Jinda Lu
卢金达
