核心概念
Proposing a novel Depth-Wise Graph Neural Network (DepWiGNN) to address multi-hop spatial reasoning challenges by operating over the depth dimension of the graph.
摘要
DepWiGNN introduces a unique node memory scheme leveraging TPR mechanism, enabling efficient long dependency collection without excessive layer stacking. Experimental results demonstrate its superiority in capturing long dependencies and outperforming existing methods on challenging datasets.
統計資料
Spatial reasoning in text is crucial for real-world applications.
DepWiGNN outperforms existing spatial reasoning methods on challenging datasets.
DepWiGNN excels in multi-hop spatial reasoning tasks.
DepWiGNN surpasses classical graph convolutional layers in capturing long dependencies.
引述
"Graph neural networks have showcased exceptional proficiency in inducing and aggregating symbolic structures."
"DepWiGNN introduces a novel node memory scheme to tackle multi-hop spatial reasoning challenges."
"Experimental results show that DepWiGNN outperforms existing spatial reasoning methods."