Graph Neural Networks
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
This paper introduces ChiGAD, a novel spectral Graph Neural Network (GNN) framework designed for heterogeneous graph anomaly detection. It addresses key challenges like capturing abnormal signals across diverse meta-paths, retaining high-frequency content during dimension alignment, and effectively learning from difficult anomaly samples amidst class imbalance. ChiGAD leverages a new Chi-Square filter, interactive meta-graph convolution, and a contribution-informed cross-entropy loss. Experimental results on public and industrial datasets show ChiGAD's superior performance over state-of-the-art models, with a homogeneous variant (ChiGNN) also excelling.
Executive Impact
Enhanced Anomaly Detection for Critical Enterprise Systems
ChiGAD significantly improves the accuracy and reliability of anomaly detection in complex, heterogeneous enterprise environments. By identifying subtle fraudulent patterns and system anomalies that traditional GNNs miss, it directly reduces financial losses, enhances security, and optimizes operational efficiency. Its ability to handle diverse data types and imbalanced datasets makes it an invaluable tool for financial services, cybersecurity, and supply chain integrity.
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AUROC Score (ChiGAD) - ACM
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AUPRC Improvement - R-I
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Recall Improvement - R-II
Deep Analysis & Enterprise Applications
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Graph Neural Networks
The research delves into **Graph Neural Networks** for enhanced anomaly detection, especially in complex, heterogeneous data structures. Key findings include:
2.55%
AUROC % Improvement over SOTA (ACM)
ChiGAD's Multi-Graph Filter Process
Meta-Path Graph Generation
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Frequency Focus Estimation
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Chi-Square Filter Assignment
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Semantic Representation Learning
| Model | Key Features | ChiGAD Advantages |
|---|---|---|
| Operational-Model (GCN-based) | GCN-based, manually selected meta-paths, low-pass filter property. |
|
| PSHGCN (Multi-graph filter) | Polynomial multi-graph filter, positive semi-definite constraint. |
|
| BWGNN (Wavelet GNN) | Beta wavelet filters, right-shift phenomenon. |
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Impact in Financial Fraud Detection (R-I, R-II Datasets)
On industrial financial datasets R-I and R-II, ChiGAD demonstrated significant improvements in fraud detection. For instance, on R-I, it achieved a 31.34% AUPRC improvement and on R-II, a 26.09% Recall improvement over state-of-the-art baselines. This highlights ChiGAD's ability to discern subtle, anomalous transaction patterns in real-world, highly imbalanced financial networks, substantially reducing false negatives and protecting against sophisticated fraud schemes.
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Annual Savings
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