Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks
Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on first-order neighbors have long faced several fundamental contradictions: increasing depth leads to over-smoothing, long-range dependencies cause over-compression, fixed neighborhoods restrict the receptive field, and on heterophilous graphs, topological neighbors become a source of noise. Although many works have addressed these issues individually, few mechanisms can simultaneously alleviate all of these challenges. To address the aforementioned problems, we propose a Position Encoding-Based Deformable Spatial Aggregation Module (PEBDSAM) that solves them a...
| Authors | Jinhua Wu, Xinliang Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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