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positional encoding

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papersTODAY 04:00 UTC

arXiv Paper Introduces PE-Based Deformable Graph Neural Networks

A new arXiv preprint proposes deformable graph neural networks built on positional encoding to tackle long-standing limits of message passing over first-order neighbors. The authors note that conventional GNNs struggle as depth increases, leading to over-smoothing and difficulty capturing long-range structure. The work targets graph-structured data in real-world settings where deeper models are needed.

graph-neural-networks/arxiv/research/over-smoothing