🤖 AI Summary
This work addresses the high computational cost of graph neural networks (GNNs) in semi-supervised node classification, their reliance on the homophily assumption, and the limited adaptability of existing training-free methods to heterophilous graphs. To overcome these challenges, the authors propose a novel training-free, efficient label propagation framework that unifies handling of both homophilous and heterophilous graph structures. The method introduces the local clustering coefficient into an adaptive propagation kernel for the first time and leverages the geometric median to construct robust class prototypes. Extensive experiments demonstrate that the proposed approach achieves accuracy on par with or even surpassing that of trainable GNNs across multiple benchmark datasets, while significantly improving computational efficiency.
📝 Abstract
Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by significant computational overhead and reliance on strong homophily assumptions. Traditional GNNs require expensive iterative training and multi-layer message passing, while existing training-free methods, such as Label Propagation, lack adaptability to heterophilo\-us graph structures. This paper presents \textbf{F$^2$LP-AP} (Fast and Flexible Label Propagation with Adaptive Propagation Kernel), a training-free, computationally efficient framework that adapts to local graph topology. Our method constructs robust class prototypes via the geometric median and dynamically adjusts propagation parameters based on the Local Clustering Coefficient (LCC), enabling effective modeling of both homophilous and heterophilous graphs without gradient-based training. Extensive experiments across diverse benchmark datasets demonstrate that \textbf{F$^2$LP-AP} achieves competitive or superior accuracy compared to trained GNNs, while significantly outperforming existing baselines in computational efficiency.