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Arxiv
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Towards Scalable and Deep Graph Neural Networks via Noise Masking
- Graph Neural Networks (GNNs) have achieved remarkable success in graph mining tasks.
- Scaling GNNs to large graphs is challenging due to high computational and storage costs.
- Proposed random walk with noise masking (RMask) module to enable deeper GNN exploration while preserving scalability.
- Experimental results show improved performance and trade-off between accuracy and efficiency.
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