用高频信号检测图异常，比传统方法更准。
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
- 通过捕捉图中高频信号识别异常节点
- 在四个数据集上准确率达96%以上
- 特别适合处理孤立节点，提升检测效果
图卷积网络（GCN）因其与图结构的天然契合性，广泛应用于图异常检测（GAD），显著提升了性能。然而，多数研究将GAD视为节点分类任务，依赖低通滤波或邻域特征聚合。本文提出一种新型高通图卷积网络（HP-GCN），利用高频成分检测异常，因异常会增加正常节点间网络的高频信号。此外，孤立节点缺乏交互，对图神经网络构成挑战。为此，模型将图分为孤立节点与连通子图，孤立节点通过多层感知机（MLP）学习特征，提升检测精度。在YelpChi、Amazon、T-Finance和T-Social数据集上的实验表明，所提HP-GCN分别达到96.10%、98.16%、96.46%和98.94%的异常检测准确率。结果表明，该方法优于基于空间域的GNN及使用低通、带通滤波的频域GCN方法，验证了其在提升异常检测性能方面的有效性。源代码见：https://github.com/meteor0033/High-pass_GAD.git。
原文摘要 · Abstract (English)
Graph Convolutional Network (GCN) are widely used in Graph Anomaly Detection (GAD) due to their natural compatibility with graph structures, resulting in significant performance improvements. However, most researchers approach GAD as a graph node classification task and often rely on low-pass filters or feature aggregation from neighboring nodes. This paper proposes a novel approach by introducing a High-Pass Graph Convolution Network (HP-GCN) for GAD. The proposed HP-GCN leverages high-frequency components to detect anomalies, as anomalies tend to increase high-frequency signals within the network of normal nodes. Additionally, isolated nodes, which lack interactions with other nodes, present a challenge for Graph Neural Network (GNN). To address this, the model segments the graph into isolated nodes and nodes within connected subgraphs. Isolated nodes learn their features through Multi-Layer Perceptron (MLP), enhancing detection accuracy. The model is evaluated and validated on YelpChi, Amazon, T-Finance, and T-Social datasets. The results showed that the proposed HP-GCN can achieve anomaly detection accuracy of 96.10%, 98.16%, 96.46%, and 98.94%, respectively. The findings demonstrate that the HP-GCN outperforms existing GAD methods based on spatial domain GNN as well as those using low-pass and band-pass filters in spectral domain GCN. The findings underscore the effectiveness of this method in improving anomaly detection performance. Source code can be found at: https://github.com/meteor0033/High-pass_GAD.git.
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