Summary of High-pass Graph Convolutional Network For Enhanced Anomaly Detection: a Novel Approach, by Shelei Li et al.
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
by Shelei Li, Yong Chai Tan, Tai Vincent
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed High-Pass Graph Convolution Network (HP-GCN) for Graph Anomaly Detection (GAD) offers a novel approach by leveraging high-frequency components to detect anomalies. The model segments the graph into isolated nodes and connected subgraphs, with isolated nodes learning features through Multi-Layer Perceptron (MLP). This enhances detection accuracy. Evaluated on YelpChi, Amazon, T-Finance, and T-Social datasets, HP-GCN achieves anomaly detection accuracy of 96.10%, 98.16%, 96.46%, and 98.94% respectively, outperforming existing GAD methods based on spatial domain Graph Neural Network (GNN) and spectral domain GCN. This method demonstrates the effectiveness in improving anomaly detection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach to detect anomalies in graphs using High-Pass Graph Convolution Networks (HP-GCN). They try to fix a problem with existing methods that don’t work well when some nodes are alone without any connections. To solve this, they split the graph into isolated nodes and groups of connected nodes. The isolated nodes learn their own features, which helps detect anomalies better. They tested their method on four different datasets and it worked really well, even better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Gcn » Gnn » Graph neural network
