Breaking the confinement of fixed nodes: A causality-guided adaptive and interpretable graph neural network architecture

Chao Wang, Xuancheng Zhou, Zihao Wang, Yang Zhou

Abstract: Highlights•Consider input-feature correlation with node degree to enhance GNN performance.•A causality-guided GNN architecture with self-adaptation is presented.•Theoretical analyses and experiments validate the sophistication and effectiveness of C-GNN.