Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion

Researchers propose a K-Hop Gaussian diffusion kernel to enhance graph neural networks, improving information propagation in noisy or complex graphs. The method outperforms traditional message-passing GNNs and existing diffusion kernels on benchmark datasets.

arXiv:2606.18317v1 Announce Type: new Abstract: Most graph neural network GNN cores rely on graph convolutions, typically implemented as message passing between direct single-hop neighbors. In many real-world graphs, edges can be noisy or poorly defined, limiting information propagation to local neighborhoods. Existing diffusion kernels, such as Personalized PageRank PPR and Heat Kernel, alleviate this issue through global propagation, but still struggle with complex local structures and distant node noise. To address these limitations, we propose a K-Hop Gaussian KHG diffusion kernel as a preprocessing module for graph data. KHG introduces multi-hop diffusion with Gaussian weighting for remote nodes, balancing local and global information propagation before applying standard GNNs. Experiments on multiple benchmark datasets demonstrate that KHG significantly outperforms traditional message-passing GNNs, as well as PPR and Heat Kernel diffusion, particularly in noisy or structurally complex graphs.