Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals

Despite the great successes of graph neural networks (GNNs) in modeling and analyzing complex graph data, the inductive bias of locality assumption, which involves exchanging information only within neighboring connected nodes, restricts GNNs in capturing long-range dependencies and global patterns in graphs.