High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
This work addresses anomaly detection in graph data, which is crucial for applications like fraud detection, but it is incremental as it builds on existing GCN approaches by focusing on high-pass filters and handling isolated nodes.
The paper tackled the problem of graph anomaly detection by proposing a High-Pass Graph Convolutional Network (HP-GCN) that leverages high-frequency signals to detect anomalies, achieving accuracies of 96.10% to 98.94% on benchmark datasets and outperforming existing methods.
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.