Label Propagation for Graph Label Noise

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One major limitation is that they assume the graph is homophilous and the labels are smoothly distributed. Nevertheless, real-world graphs may contain varying degrees of heterophily or even be heterophily-dominated, leading to the inadequacy of current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a simple yet efficient algorithm, denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of correct labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising "effect". Finally, we conduct experiments on 10 benchmark datasets under varying graph heterophily levels and noise types, comparing the performance of LP4GLN with 7 typical baselines. Our results illustrate the superior performance of the proposed LP4GLN.