GNN Defender: Overcoming Mixed Noise Through Cross-Correction Over Nodes and Edges

Graph Neural Networks (GNNs) have achieved remarkable success in processing graph data. However, when graph contains node label noise (label error) and graph structural noise (edge error), the performance of GNNs will severely decrease. In the learning with noisy label, the methods based on sample selection and label generation reveal promising outlook. Unfortunately, most of these methods only focus on single node label nosie or graph edge noise, which cannot handle both two types of noise at the same time. Furthermore, these methods also lack measures for error accumulation during training. In this work, we propose a simple yet efficient method, named GNN Defender (GDF), to address challenges of both types of noises through cross-correction and two regularization technologies. Specifically, GDF consists of two networks (Nnet and Enet), which Nnet is responsible to predict node category and Enet is used to predict the connected probability of two nodes. For sample selection, the mean distribution distance between node category prediction and node original label is regard as the selecting indicator. Then, we consider node labels with distances less than the mean as clean labels. Similar to node, replacing indicator with the mean cosine similarity of connected to Adaptively complete clean edge selection. For cross correction, we use the clean probabilities of the edges between connected nodes as weights to generate pseudo labels for target nodes on the subgraph. The pseudo labels generated by edge information carry more credible supervision information, thereby completing the correction of node noise labels. Next, we compare the label consistency between connected node to achieve the noisy edge correction. It is worth noting that the clean probabilities of node labels are used to smooth graph structure, improving the edge correction quality. Finally, we propose category consistency regularization and subgraph clustering regularization to weaken the impact of error accumulation. Extensive experiments on single noise and mixed noise datasets show that our proposed framework far outperforms current baseline methods, with improvements ranging from 1.43% to 14.72%.