Simplified and Adjustable Graph Diffusion Neural Networks

Graph Convolutional Networks (GCNs) have become a widely used framework for learning from graph-structured data due to their efficiency and performance in tasks such as node classification and link prediction. However, conventional GCNs are limited by a small receptive field, typically restricted to 1–2 hops, which prevents them from capturing long-range dependencies. Graph diffusion methods address this limitation by integrating multi-hop information, but they often introduce high computational costs and over-smoothing issues. To overcome these challenges, we propose a Simplified and Adjustable Graph Diffusion model. Our method employs a predefined diffusion stage and introduces two adaptive parameters: a distance parameter that specifies the diffusion depth and a diffusion control parameter that dynamically adjusts edge weights based on inter-node distances. This approach reduces computational overhead while enabling more effective information propagation. Extensive experiments on benchmark datasets demonstrate that our model achieved an average improvement of 1.9 percentage points in AUC for link prediction and 2.2 percentage points in accuracy for semi-supervised classification tasks. The improvements are particularly significant when leveraging structural information from distant nodes. The proposed framework strikes a balance between accuracy and efficiency, offering a practical alternative for scalable graph learning applications.