通过跨注意力子图表示缓解图神经网络的长程信息压缩问题
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
- 用跨注意力机制捕捉长程子图结构，增强节点表征
- 在多个基准数据集上分类准确率显著提升
- 适合处理密集异质图中长距离依赖的场景
图神经网络在众多现实场景中表现优异，但面临过度压缩问题：长程信息在有限的消息传递路径中被扭曲，导致全局上下文丢失，尤其在图的密集和异质区域性能下降。为此，我们提出一种新框架，通过跨注意力的一致性子图表示丰富节点嵌入，以缓解过度长程依赖的影响。该方法强调长程信息中的紧密结构，同时剔除噪声或无关连接，在不加重瓶颈通道的前提下保留关键全局上下文，有效缓解了过度压缩问题。在多个基准数据集上的大量实验表明，该模型在分类准确率上持续优于标准基线方法。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited message-passing pathways. This bottleneck limits their ability to capture essential global context and decreases their performance, particularly in dense and heterophilic regions of graphs. To address this issue, we propose a novel graph learning framework that enriches node embeddings via cross-attentive cohesive subgraph representations to mitigate the impact of excessive long-range dependencies. This framework enhances the node representation by emphasizing cohesive structure in long-range information but removing noisy or irrelevant connections. It preserves essential global context without overloading the narrow bottlenecked channels, which further mitigates oversquashing. Extensive experiments on multiple benchmark datasets demonstrate that our model achieves consistent improvements in classification accuracy over standard baseline methods.
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