ADC-GNN improves few-shot graph fraud detection by combining diffusion-guided feature augmentation, contrastive learning, and multi-hop spectral attention, showing gains on public benchmarks under 1% labeled data.
Graph Wavelet Neural Network
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abstract
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
ADC-GNN improves few-shot graph fraud detection by combining diffusion-guided feature augmentation, contrastive learning, and multi-hop spectral attention, showing gains on public benchmarks under 1% labeled data.